The Islamic University Gaza

Higher Education Deanship

Faculty of Engineering

Civil Engineering Department

Infrastructure Program

غزة – اإلسالمية الجامعة

العليا الدراسات عمادة

الهندسة كلية

قسم الهندسة المدنية

هندسة البنى التحتية

بطريقة اتوماتيكية الجويةاستخراج المعالم من الصورتقييم طرق

EVALUATION OF AUTOMATIC FEATURE EXTRACTION

TECHNIQUES FROM IMAGERY

Submitted by:

Wesam A. Alashqar

Supervised by:

Dr. Maher A. El-Hallaq

A Thesis Submitted in Partial Fulfillment of Requirements for the Degree of Master of

Science in Infrastructure - Civil Engineering.

م ۲۰۱۳-هـ ۱٤۳٤

i

DEDICATION

To my parents, who gave me life twice, once when I was born, and again when they

thought I have to go to school..

To martyrs: my uncle Abed Al Azeez and uncle Mohammad (Abo Amer) and my

cousin Mohammad (Abo Youssef)

To my wife, Feda, who always pushed me to finish this thesis..

To my sons, Mohammad and Ahmed, whom I do all of this for..

To my brothers, Hosam, Ehab and Eyad and, and my sisters, Samaher, Sabreen, and

Hadeel..

To my teachers, along my academic trip extending over 20 years..

To my country, Palestine, united Palestine…

I dedicate this work.

ii

ACKNOWLEDGEMENT

I would like to express my deep acknowledgement to everyone who helped me finish

this work, especially Dr. Maher El-Hallaq, my thesis supervisor, who always gave

me spiritual support and technical guidance during the research work.

I would thank the Municipality of Gaza for providing data (Maps) related to research.

iii

7Bملخص الدراسة

في المعقدة المهام إلنجاز بعد عن االستشعار بيانات استخدام إلى الحاجة شهدت الماضية القليلة السنوات مدى على

الجوية الصور ألن صعبة مهمة هي الصور من الخرائط رسم معالم استخراجتعتبر .استخراج المعالم من الصور

المعلومات نظم أنشطة من للعديد جدا تعتبر مهمة المعالم من الصور استخراج. وغامضة ومعقدة، بطبيعتها، صاخبة

تعتمد العملية هذه. المكانية الجغرافية البيانات تكامل واالرجاع الجغرافي وكذلك التحديث، مثل GIS الجغرافية

مكلفة عملية GIS بيانات نظم المعلومات الجغرافية قواعد تطوير يجعل مما البشرية، العمالة على كبيرا اعتمادا

الوقت من كبير بشكل تقلل أن يمكن المعالم بشكل آلي استخراج امكانية. يدويا بها القيام عند طويال وقتا وتستغرق

. البيانات قواعد وتطوير وتحديثها، البيانات على الحصول في والتكلفة

) GISم المعلومات الجغرافية (ستخدم نظبابلديات قطاع غزة والمؤسسات سنوات العشر األخيرة، بدأت بعض في ال

المعالم تعامل مع البيانات المتوفرة من الصور الجوية واستخراج الأصبح من الضروري في المشاريع. ولذلك

فلسطين كل الطرق المستخدمة في استخراج بر الستخدامها في التحليل والتخطيط وصنع القرار. في الوقت الحاض

).الرسم اليدويتقليدية (ال تزال المعالم من الصور الجوية

. المعالم من الصور ستخراجإل المستخدمة الصور أنواع عن عامة لمحة وتقديم تم استعراض األطروحة هذه في

، اإلجراءات لهذه والنوعية الكمية للدقة تقييم مع المعالم من الصور ستخراجإل المستخدمة األساليب وأيضا

مثل عدد من التقنيات المحسنة والتى تم تطويرها واستخدامها في بعض البرامجوتقييم دراسة باالضافة إلى

ERDAS 2013, ENVI 5.0, Barista 2.3.1 استخراج المعالم بشكل آلي مع دراسة يةمع دراسة سير عمل

.ومقارنة النتائج

صى الباحث بالعمل على تطوير أو تحسين خوارزميات ترفع من جودة البيانات التى يتم استخراجها في النهاية أو

.بقدر المستطاع والتعاون من فرق بحثة محلية وعالمية في هذا المجال

iv

ABSTRACT

Features extraction from raster images is very important of many GIS activities such as

GIS updating, geo-referencing, and geospatial data integration. This process depends

heavily on human labor, which makes GIS database development an expensive and

time-consuming operation when performed manually. Automated feature extraction can

significantly reduce the time and cost of data acquisition and update, database

development and turnaround time.

For the last ten years, some of the Gaza Strip municipalities and institutions have begun

using geographic information system (GIS) in their projects. Therefore, it was become

necessary to deal with available data from aerial images and extract features to be used

in analysis, planning and decision-making. Nowadays in Palestine, all methods used in

feature extraction are still traditional (manual methods).

Recently, the need for using remote sensing data to accomplish the complex task of

automatic extraction of features has significantly increased. Among the sensor systems

currently used for mapping can be highlighted the recent launches of new orbital

satellites. Extracting cartographic objects from images is a difficult task because aerial

images are inherently noisy, complex, and ambiguous.

This thesis reviews and provides an overview of the types of imagery being used for

feature extraction. It also describes the methods used for feature extraction as well as

the quantitative and qualitative accuracy assessment of these procedures. Number of

optimization techniques that have been developed in stand-alone programs are studied

such as ERDAS Imagine, ENVI and Barista to automate the extraction with evaluating

and comparing the feature extraction workflow of these programs and feature extraction

results.

Finally, it is recommended to continue working on the development or improvement of

existing algorithms to enhance the percentage of accuracy and speed of data, which are

extracted as much as possible and the cooperation with local and global teams in this

field.

v

TABLE OF CONTENTS

DEDICATION ................................................................................................................. i

ACKNOWLEDGEMENT .............................................................................................. ii

الدراسة ملخص ...................................................................................................................... iii

ABSTRACT .................................................................................................................... iv

TABLE OF CONTENTS ............................................................................................... v

LIST OF ABBREVIATIONS ....................................................................................... ix

LIST OF TABLES .......................................................................................................... x

LIST OF FIGURES ....................................................................................................... xi

1 CHAPTER 1: INTRODUCTION

1.1 Scope ................................................................................................................. 1

1.2 Background ....................................................................................................... 1

1.3 Problem Statement ............................................................................................ 2

1.4 Research Aim and Objectives ........................................................................... 2

1.5 Methodology ..................................................................................................... 3

1.6 Research Structure ............................................................................................ 4

2 CHAPTER 2: REMOTE SENSING & DIGITAL IMAGE PROCESSING

2.1 Scope ................................................................................................................. 5

2.2 Remote Sensing ................................................................................................ 5

2.2.1 Historic Overview ....................................................................................... 5

2.2.2 Principles of Remote Sensing ..................................................................... 6

vi

2.2.3 Electromagnetic Radiation .......................................................................... 8

2.2.4 Electromagnetic Spectrum .......................................................................... 8

2.2.5 Sensors and Platforms ................................................................................. 9

2.2.5.1 Type of Satellite Sensors ...................................................................... 9

2.2.5.2 Satellite Sensor Characteristics ........................................................... 10

2.3 Digital Image Processing ................................................................................ 12

2.3.1 Pre-Processing .......................................................................................... 13

2.3.2 Image Enhancement .................................................................................. 15

2.3.3 Image Transformation ............................................................................... 15

2.3.4 Image Classification and Analysis ............................................................ 16

2.3.4.1 Supervised Classification .................................................................... 17

2.3.4.2 Unsupervised Classification ............................................................... 18

2.3.5 Available Feature Extraction Methods ..................................................... 19

2.3.5.1 Manual Digitizing ............................................................................... 19

2.3.5.2 Automatic Digitizing .......................................................................... 20

2.3.5.2.1 Object-Based Feature Extraction .................................................. 26

2.3.5.2.2 Pixel-Based Feature Extraction ..................................................... 27

2.4 Conclusion ...................................................................................................... 35

3 CHAPTER 3: LITERATURE REVIEW

3.1 Scope ............................................................................................................... 36

3.2 Background ..................................................................................................... 36

3.3 Types of Imagery Used in Feature Extraction ................................................ 38

vii

3.4 Feature Extraction ........................................................................................... 39

3.4.1 Manual Versus Automated Extraction ...................................................... 39

3.4.2 A Feature Model ....................................................................................... 40

3.5 Techniques for Feature Extraction .................................................................. 41

3.5.1 Mathematical Morphology ....................................................................... 41

3.5.2 Hough Transform ...................................................................................... 42

3.5.3 Multi-Resolution Techniques ................................................................... 44

3.5.4 Template Matching ................................................................................... 44

3.5.5 Dynamic Programming ............................................................................. 45

3.5.6 Particle Swarm Optimization .................................................................... 46

3.5.7 Pixel Swapping ......................................................................................... 47

3.5.8 LSB Snake ................................................................................................ 47

3.5.9 Edge Detection .......................................................................................... 48

3.6 Knowledge Integration ................................................................................... 49

3.7 Classification-based Feature Extraction ......................................................... 50

3.8 Assessing Feature Extraction Techniques ...................................................... 50

3.9 Conclusion ...................................................................................................... 51

4 CHAPTER 4: METHODS EVALUATION

4.1 Scope ............................................................................................................... 52

4.2 Data Collection and Preparation .................................................................... 52

4.2.1 Data Collection ......................................................................................... 52

4.2.2 Image Pre-Processing ............................................................................... 54

viii

4.2.2.1 Geometric Correction ......................................................................... 54

4.2.2.2 Radiometric Correction ....................................................................... 55

4.2.2.3 Image Enhancement ............................................................................ 56

4.3 Feature Extraction Methods ............................................................................ 57

4.3.1 Manual Digitizing ..................................................................................... 57

4.3.2 Automatic Digitizing ................................................................................ 60

4.3.2.1 Object-Based Feature Extraction ........................................................ 60

4.3.2.2 Pixel-Based Feature Extraction .......................................................... 71

4.4 Results and Discussion ................................................................................... 73

5 CHAPTER 5: CONCLUSION AND RECOMMENDATIONS

5.1 Conclusion ...................................................................................................... 79

5.2 Recommendations ........................................................................................... 79

REFERENCES .............................................................................................................. 80

ix

LIST OF ABBREVIATIONS

AVIRIS Airborne Visible InfraRed Imaging Spectrometer

CAD Computer Aided Design

CCM Canada Centre for Mapping

CIR Colour Infrared

DN Digital Number

ENVI® The Environment for Visualizing Images

FOV Field of View

GCPs Ground Control Points

GIS Geographic Information System

GSD Ground Sample Distance

HT Hough Transform

HYDICE HYperspectral Digital Imagery Collection Experiment

IFOV Instantaneous Field of View

KNN K Nearest Neighbor

LiDAR Light Detection And Ranging Data

MM Mathematical Morphology

NASA National Aeronautics and Space Administration

NDVI Difference Vegetation Index

PCA Principal Components Analysis

PSO Particle Swarm Optimization

SAR Synthetic-Aperture Radar

SFP Single Feature Probability

SVM Support Vector Machine

TM Landsat Thematic Mapper

USGS U.S. Geological Survey

VLS Visual Learning Systems

x

LIST OF TABLES

Table 2.1: Characteristics of various optical remote sensing systems.. .......................... 12

Table 4.1: Results of feature extraction methods ………………………….….……….76

Table 4.2: Comparison between area of extracted sample of buildings ......................... 76

Table 4.3: Time spent to extract features from images by different methods ................ 77

Table 4.4: Summary of overall comparison between feature extraction methods ......... 77

xi

LIST OF FIGURES

Figure 1.1: Methodology flowchart .................................................................................. 3

Figure 2.1: Elements of remote sensing system ............................................................... 7

Figure 2.2: Electromagnetic radiation components .......................................................... 8

Figure 2.3: Electromagnetic spectrum components .......................................................... 9

Figure 2.4: Platforms for remote sensors .......................................................................... 9

Figure 2.5: Type of satellites sensors .............................................................................. 10

Figure 2.6: Spatial resolution .......................................................................................... 11

Figure 2.7: Digital image pixels ..................................................................................... 13

Figure 2.8: Resampling type of images ......................................................................... 14

Figure 2.9: Concept of classification of remotely rensed data ....................................... 16

Figure 2.10: Steps in supervised classification .............................................................. 17

Figure 2.11: Unsupervised classification flow diagram ................................................. 18

Figure 2.12: Methodology of pixel-based and object based processing ........................ 21

Figure 2.13: Optimal thresholding .................................................................................. 24

Figure 2.14: Comparison between edge detection at different thresholding levels ........ 25

Figure 2.15: Concept of object-based feature extraction ................................................ 26

Figure 2.16: Concept of pixel-based feature extraction .................................................. 27

Figure 2.17: Type of step edges ...................................................................................... 28

Figure 2.18: Steps of edge extraction using the Canny operator .................................... 29

Figure 2.19: Applying Gaussian averaging .................................................................... 30

xii

Figure 2.20: Illustration of gradient calculation in Canny operator ............................... 31

Figure 2.21: Semicircle edge orientation ........................................................................ 33

Figure 2.22: Non maximal suppression procedure ......................................................... 34

Figure 3.1: Combining erosion and dilation to produce an opening or a closing ........... 42

Figure 3.2: Illustration of parameters of hough transform .............................................. 43

Figure 3.3: Template matching technique ...................................................................... 45

Figure 3.4: An LSB-snake of a road segment ................................................................. 48

Figure 3.5: Type of edge detection algorthiems ............................................................. 49

Figure 4.1: Study Area (1) .............................................................................................. 53

Figure 4.2: Study Area (2) .............................................................................................. 53

Figure 4.3: Study Area (3) .............................................................................................. 54

Figure 4.4: Geo-referencing method & toolbar in ARCGIS 10.1 .................................. 55

Figure 4.5: Noise reduction of Study Area (2) ............................................................... 56

Figure 4.6: Histogram of study areas .............................................................................. 56

Figure 4.7: Feature extraction methods and programs ................................................... 57

Figure 4.8: Create feature class in personal geodatabase using Arc-catalge .................. 58

Figure 4.9: Editor toolbar in Arc-map ............................................................................ 58

Figure 4.10: Manual tracking and digitizing edges of the buildings .............................. 59

Figure 4.11: Manual feature extraction of study areas ................................................... 59

Figure 4.12: Feature extraction workflow of ENVI 5.0 ................................................. 61

Figure 4.13: Object based feature extraction toolbox ..................................................... 62

Figure 4.14: Image segmentation result at different levels ............................................. 62

xiii

Figure 4.15: Merging segments result at different levels ............................................... 63

Figure 4.16: Optimal segmentation level 82 and merge level 95 ................................... 63

Figure 4.17: Classfication method and building extraction ............................................ 64

Figure 4.18: Erdas objective process work flow diagram .............................................. 65

Figure 4.19: Configurtions tree view menu of imagine objective ................................. 66

Figure 4.20: Training areas of imagine objective ........................................................... 67

Figure 4.21: Comparison between edge detection at different thresholding levels ........ 68

Figure 4.22: Steps of feature extarction using Erdas ojective 2013 ............................... 70

Figure 4.23: Extract features using Barista program ...................................................... 71

Figure 4.24: Feature extraction dialog of Barista program ............................................ 72

Figure 4.25: Feature Extraction using Canny Operator .................................................. 73

Figure 4.26: Surveying layout of Tayba building ........................................................... 73

1

1 CHAPTER 1: INTRODUCTION

1.1 Scope

This chapter is intended to give a brief overview of research problem emphasizing the

importance of automatic extraction features from imagery to minimizing cost and time

of data acquisition and update GIS database. It gives a description of the research

importance, scope, objectives, methodology, deliverables, as well as thesis organization.

1.2 Background

Humans have sought to extract information from imagery ever since the first

photographic images were acquired. As early as the mid nineteenth century, the French

Army Corps of Engineers experimented with using aerial photographs for

reconnaissance and mapping (Wolf and Dewitt, 2000).

Features extraction from raster images is very important of many GIS activities such as

GIS updating, geo-referencing, and geo-spatial data integration. This process depends

heavily on human labor, which makes GIS database development an expensive and

time-consuming operation when it is performed manually. Automated feature extraction

can significantly reduce the time, cost of data acquisition, update, database development

and turnaround time. Therefore, automated features extraction has been a hot research

topic over the past two decades.

Satellite images contain very rich information and when fused with vector map can

provide a comprehensive view of a geographical area. Google, Yahoo, and Virtual Earth

maps are good examples to show the power of such high resolution images. However,

high resolution images pose great challenges for automatic feature extraction due to the

inherent complexities. First, a typical aerial photo captures everything in the area such

as buildings, cars, trees, etc. Second, different objects are not isolated, but mixed and

interfere with each other, e.g., the shadows of trees on the road, building tops with

similar materials.

2

In addition, the light and weather conditions have big impact over images. Therefore, it

is impossible to predict what and where objects are, and how they look like in a raster

image. All these uncertainties and complexities make the automatic extraction very

difficult.

1.3 Problem Statement

After increase in available imagery in recent years, it followed the launch of several

airborne and satellite photography which are necessary for engineering, planning and

other science fields, it's efficiently dealing with the vast amount of available data

necessitates an increase in automation, while still taking advantage of the skills of a

human operator, some problems were appeared in manual feature extraction from these

images such as accuracy and speed requirements. But, the automated feature extraction

of aerial and satellite images are still at the stage of fundamental research in institutes

and universities in developed countries.

Ten years ago in Gaza Strip, some of municipalities and institutions have begun using

geographic information system (GIS) in projects. Therefore, it was become necessary to

deal with available data from aerial images and extract features to be used in analysis,

planning and decision-making. Nowadays in Palestine, all methods which used in

feature extraction are still traditional (manual methods).

1.4 Research Aim and Objectives

This research aims to investigate and evaluate the current methods that enable as to

extract features from aerial or satellite images automatically.

To achieve this aim, the following objectives are to be determined:

a) Reviewing the available method that can be used to extract specific features such

as (polylines, polygons, and points) from images.

b) Performing an objective evaluation of such methods.

c) Exploring the future developments that is needed to enhance extracting.

d) Showing the integration between GIS and extracted feature database of images.

3

1.5 Methodology

The methodology stages that can be used to achieve the study aim are outlined through

the following lines (see Figure 1.1):

- Literature review on currently available techniques for feature extraction from satellite

images such as (Mathematical Morphology, Hough Transform, Multi-Resolution

Techniques, and Template matching, etc) and assessing these feature extraction

techniques.

- Data collection of aerial photos and satellite images from number of sources that

provide these images (Landsat, IKONOS, Spot, QuickBird satellites).

- Study some of characteristics of aerial photos and satellite images such as (spatial

resolution, spectral bands, types, image surface area ,etc).

- Use existing algorithms to extract features from images using environment

programming.

- Comparison between traditional and automatic methods depending on a set of criteria

to determine the accuracy and success percentage of theses methods.

- Concluding remarks and recommendations.

Figure 1.1: Methodology flowchart

Problem Identification

Literature Review

Data Collection

Data Preparation

Data Extraction Manual Automated

Data Analysis & Evaluation

Conclusion &

Recommendations

4

1.6 Research Structure

This research is oriented into five chapters.

Chapter 1 is intended to give a brief overview of research problem emphasizing the

importance of automatic extraction features from imagery to minimizing cost and time

of data acquisition and update GIS database. It gives a description of the research

importance, scope, objectives, methodology, deliverables, as well as thesis organization.

Chapter 2 describes historic overview of remote sensing technology and its

development stages. It also illustrates the basic of remote sensing and how it works with

more details about each of these elements. This chapter also discusses the types and

characteristics of satellite sensors as well as the most of the common image processing

available in image analysis systems.

Chapter 3 gives a background of historical stages for feature extraction at the beginning

of imagery were acquired by humans, and provides an overview of the types of imagery

being used for feature extraction. This chapter also describes the methods and

techniques that used for feature extraction the quantitative and qualitative accuracy

assessment of these procedures.

Chapter 4 contains detailed description of the steps of the methodology of the research.

It includes steps strategy beginning from data collection of imagery and image

processing of these image to use an existing of automatic feature extraction methods

and methods evaluation.

Finally, Chapter 5 summarizes the conclusion outcomes and outlines the significant

recommendations.

5

2 CHAPTER 2: REMOTE SENSING & DIGITAL IMAGE

PROCESSING

2.1 Scope

This chapter describes historic overview of remote sensing technology and its

development stages. It also illustrates the basic of remote sensing and how it works with

more details about each of these elements. This chapter discusses the types and

characteristics of satellite sensors as well as the most common available image

processing techniques used in image analysis systems.

2.2 Remote Sensing

Remote sensing is the acquisition of information about an object or phenomenon

without making physical contact with the object. In modern usage, the term generally

refers to the use of aerial sensor technologies to detect and classify objects on Earth

(both on the surface, in the atmosphere and oceans) by means of propagated

signals (e.g. electromagnetic radiation emitted from aircraft or satellites) (Aggarwal,

2003).

2.2.1 Historic Overview

Gaspard Tournachon took a slanting photograph of a small village near Paris from a

balloon in 1859. After this picture, the time of remote sensing and earth observation

had started. Other people all over the world soon followed by his example. During the

Civil War in the United States, aerial photography from balloons played an important

role to reveal the defence positions in Virginia (Colwell, 1983).

The next period of development was in Europe not in the United States. Aeroplanes

were used a large scale for photoreconnaissance during World War I. With fast

development of airborne military industries, aircraft proved to be more stable and more

reliable platforms for earth observation than balloons. In the period between World War

I and World War II, a start was made with the civilian use of aerial photos. Application

fields of airborne photos included agriculture, geology, forestry, and cartography. After

these fast developments in technology and industry, application lead to much improved

cameras, films and interpretation equipment. During World War II, most important

6

developments of aerial photography and photo interpretation was accrued. During this

time span, the development of other imaging systems such as near-infrared

photography; thermal sensing and radar took place. Thermal-infrared and Near-infrared

photography proved very valuable to separate real vegetation from disguise. Military

purposes not for civilian purposes was used the first successful airborne imaging radar

but proved valuable for darkness bombing. As such the system was called by the

military ‘plan position indicator’ and was developed in Great Britain in 1941

(Aggarwal, 2003).

Remote sensing systems continued to grow from the systems developed after the wars

in the 1950s. Colour infrared (CIR) photography was discovered to be of great use for

the plant sciences. In 1956, Colwell conducted experiments for the recognition and

classification of vegetation types and the detection of damaged and diseased or stressed

vegetation using of CIR. From 1950s up to now the significant progress in radar and

sensing technology was achieved (Aggarwal, 2003).

2.2.2 Principles of Remote Sensing

Remote sensing, also called earth observation, is the science (and to some extent, art)

can be broadly defined as any process whereby information is gathered about an object,

area or phenomenon without being in contact with it .This is done by sensing and

recording reflected or emitted energy and processing, analyzing, and applying that

information. Our eyes are an excellent example of a remote sensing device. We are able

to gather information about our surroundings by gauging the amount and nature of the

reflectance of visible light energy from some external source (such as nature light as the

sun or industry light bulb) as it reflects off objects in our field of view (Sanderson,

2001).

The process of remote sensing involves an interaction between incident radiation and

the targets of interest. This is exemplified by the use of imaging systems where the

following seven elements are involved. Note, however that remote sensing also involves

the sensing of emitted energy and the use of non-imaging sensors. Figure 2.1 shows the

essential elements of a remote sensing system which included the following lines

(Sanderson, 2001):

7

Figure 2.1: Elements of remote sensing system

1. Energy Source or Illumination (A) - energy source which illuminates or

provides electromagnetic energy to the target of interest consider the first

requirement of remote sensing.

2. Radiation and the Atmosphere (B) - as the energy travels from its source to the

target, it will come in contact with and interact with the atmosphere it passes

through. This interaction may take place a second time as the energy travels

from the target to the sensor.

3. Interaction with the Target (C) – after energy pass through atmosphere and

reach the target; it interacts with the target depending on the properties of both

the target and the radiation.

4. Recording of Energy by the Sensor (D) -we require a sensor (remotely) to

collect and record the electromagnetic radiation after the energy has been

scattered by, or emitted from the target.

5. Transmission, Reception, and Processing (E) - the energy recorded by the

sensor has to be transmitted, often in electronic form, to a receiving and

processing station where the data are processed into an image (hardcopy and/or

digital).

6. Interpretation and Analysis (F) - the processed image is interpreted, visually

and/or digitally or electronically, to extract information about the target which

was illuminated.

8

7. Application (G) – after analysing the raw information from images, the benefits

achieved when we apply the information to better understand of issues and

solving a particular problem in many fields.

2.2.3 Electromagnetic Radiation

Electromagnetic radiation consists of an electrical field which varies in magnitude, in a

direction perpendicular to the direction in which the radiation is traveling, and a

magnetic field oriented at right angles to the electrical field. Both these fields travel at

the speed of light (c) as shown in Figure 2.2 (Sanderson, 2001).

Figure 2.2: Electromagnetic radiation components

2.2.4 42BElectromagnetic Spectrum

The electromagnetic Spectrum is defined as ranges from the shorter wavelengths

(including gamma and x-rays) to the longer wavelengths (including microwaves and

broadcast radio waves) between this ranges our eyes detect visible spectrum, which

consist of three main colors (RGB) (Red – Green – Blue) from wavelengths

approximately 0.4 to 0.7 μm. Moreover there are several regions of the electromagnetic

spectrum which are useful for some remote sensing applications as shown in Figure 2.3

(Aggarwal, 2003).

9

Figure 2.3: Electromagnetic spectrum components

2.2.5 Sensors and Platforms

A sensor is a device that collect, measures and records energy reflected or emitted from

a target or surface (electromagnetic energy). Platforms for remote sensors may be

situated on the ground, on an aircraft or balloon (or some other platform within the

Earth's atmosphere), or on a spacecraft or satellite outside of the Earth's atmosphere

which is considered the famous platforms as shown in Figure 2.4 (Aggarwal, 2003).

Figure 2.4: Platforms for remote sensors

2.2.5.1 Type of Satellite Sensors

Sensors can be divided into two types depending on energy resource. Type (1): passive

sensors depend on an external source of energy, usually the sun. Photographic camera is

considered as passive sensor. Type (2): active sensors have their own source of energy;

an example would be a radar gun. These sensors send out a signal wave and measure the

amount reflected back as shown in Figure 2.5 (Sanderson, 2001).

10

Passive sensors Active sensors

Figure 2.5: Type of satellites sensors

2.2.5.2 Satellite Sensor Characteristics

The principle of most satellite sensors is to gather information about the reflected

radiation along a pathway, also known as the field of view (FOV), as the satellite orbits

the Earth. The smallest area of ground that is sampled is called the instantaneous field

of view (IFOV). The distance between the target being imaged and the sensor on

platform, plays a great role in determining the detail of information obtained and the

total area ground imaged by the sensor.

The data collected by each satellite sensor can be described in terms of spatial, spectral,

radiometric and temporal resolution (Sanderson, 2001).

- Spatial Resolution: The spatial resolution (known as ground resolution) refers to

the size of the smallest possible feature that can be detected on ground by

sensors, which depends primarily on their Instantaneous Field of View

(IFOV).For example the spatial resolution or (IFOV) of Landsat Thematic

Mapper ™ sensor is 30 m.

So, the spatial resolution depends on image applications, some of satellites

collect data at less than one meter spatial resolution but these are classified

military satellites or very expensive commercial systems such as (IKONOS and

OUIKBIRD satellites), Figure 2.6 shows an example at various spatial

resolution (30, 5, 1) meter .

11

Figure 2.6: Spatial resolution

- Spectral Resolution: defined as the number and width of spectral bands in the

sensing device, also describes the ability of a sensor to define fine wavelength

intervals. The simplest form of spectral resolution with one band.

- Radiometric Resolution: The radiometric resolution of an imaging system

describes its ability to discriminate very slight differences in energy. The

radiometric characteristics describe the actual information content in an image.

- Temporal Resolution: Temporal resolution is very important in remote sensing

system which refers to the length of time it takes for a satellite to complete one

entire orbit cycle. The actual temporal resolution of a sensor depends on a

variety of factors, including the satellite/sensor capabilities, the swath overlap,

and latitude. With temporal resolution we are able to monitor changes that take

place on the Earth's surface such as (urban development, floods, oil slicks, etc.)

(Sanderson, 2001). Landsat 5 takes 16 day to complete one entire orbit cycle,

Table 2.1 shows characteristics of various optical remote sensing systems.

12

(Song ,2001) illustrates more details about various optical remote sensing

systems. Table 2.1: Characteristics of various optical remote sensing systems

2.3 22BDigital Image Processing

Today's with high advanced technology most remote sensing data are recorded and

saved in digital format. Digital image processing may involve several procedures

including formatting and correcting of the images data, digital enhancement to facilitate

better visual interpretation, or even automated classification of targets and features

entirely by computer. A digital image that contains graphical information instead of text

or a program. Pixels or cells are the basic building blocks of all digital images. Pixels

13

are small adjoining squares in a matrix across the length and width of your digital image

as shown in Figure 2.7 (Sanderson, 2001).

Each cell contain a digital number (DN) this value of each cell is related to the

brightness, color or reflectance at that point.

Figure 2.7: Digital image pixels

Most of the common image processing functions available in image analysis systems

can be categorized into the following four categories:

• Preprocessing

• Image Enhancement

• Image Transformation

• Image Classification and Analysis

2.3.1 Pre-Processing

Pre-processing includes data operations which normally precede further manipulation

and analysis of the image data to extract specific information. These operations,

sometimes referred to as image restoration and rectification, are intended to correct for

sensor- and platform-specific radiometric and geometric distortions of data. Pre-

processing functions are generally grouped as radiometric or geometric corrections

(Sanderson, 2001).

Radiometric corrections include correcting the data for sensor irregularities and

undesirable sensor or atmospheric noise, and converting the data so they accurately

represent the reflected or emitted radiation measured by the sensor.

14

Geometric corrections include correcting for geometric distortions due to sensor-Earth

geometry variations, and conversion of the data to real world coordinates (e.g. latitude

and longitude) on the Earth's surface. Conversion data to real world coordinates are

carried by analyzing well distributed Ground Control Points (GCPs). This is done in

two steps:

- Geo-referencing: Georeference something means to define its existence

in physical space. That is establishing its location in terms of map

projections or coordinate systems. The term is used both when establishing the

relation between raster or vector images and coordinates.(Linda , 2006)

This involves the calculation of the appropriate transformation from image to

terrain coordinates using GIS software's.

- Geocoding: This step involves resembling the image to obtain a new image in

which all pixels are correctly positioned within the terrain coordinate system.

which defined as mathematical technique used to create a new version of the

image with a different width and/or height in pixels. Increasing the size of an

image is called up sampling; reducing its size is called down sampling. There

are three common resampling methods such as nearest neighborhood, bilinear

interpolation and cubic convolution as shown in Figure 2.8.(Sachs, 2001)

a) Nearest neighborhood : This method is very simple where it assigns the value of

the nearest pixel to the new pixel location.

b) Bilinear interpolation: Assigns the average value of the 4 nearest pixels to the

new pixel location.

c) Cubic convolution: Assigns the average value of the 16 nearest pixels to the new

pixel location.

Figure 2.8: Resampling type of images

15

2.3.2 Image Enhancement

Image enhancement is the modification of an image to make it easier for visual

interpretation and understanding of imagery. The advantage of digital imagery is that it

allows us to manipulate the digital pixel values in an image. Most enhancement

operations distort the original digital values. Image enhancement methods are

(Murayama, 2010):

- Contrast enhancement

In raw imagery, the useful data often populates only a small portion of the

available range of digital values (commonly 8 bits or 256 levels). Contrast

enhancement involves changing the original values so that more of the available

range is used, thereby increasing the contrast between targets and their

backgrounds. Linear contrast stretch is considered the simplest type of contrast

enhancement.

- Density slicing

Density slicing is an enhancement technique whereby the digital numbers

distributed along the x axis of an image histogram are divided into a series of

analyst specified intervals or slices.

- Frequency filtering

Spatial frequency is related to the concept of image texture Spatial filters are

designed to highlight or suppress specific features in an image based on their

spatial frequency.

- Band rationing (Spectral)

Image division or spectral rationing is one of the most common transforms

applied to image data. Image rationing serves to highlight subtle variations in the

spectral responses of various surface covers.

2.3.3 Image Transformation

Digital Image Processing offers a limitless range of possible transformations on

remotely sensed data. Image transformations typically involve the manipulation of

multiple bands of data, whether from a single multispectral image or from two or more

16

images of the same area acquired at different times (i.e. multitemporal image data)

Basic image transformations apply simple arithmetic operations to the image data.

Image transformation methods can be classified in two ways, first theoretical

transformation methods which used some of calculations such as addition and

subtraction, multiplication and division and the application of certain mathematical

models. Second empirical transformation methods such as conversion principal

components also conversion Gradient color and radiation (Sanderson, 2001).

2.3.4 Image Classification and Analysis

Classification of remotely sensed image is used to specify corresponding levels which

called class or "themes" with respect to groups with homogeneous characteristic, with

the aim of discriminating multiple objects from each other within image. The main

objective of image classification is to identify and describe, as a unique gray level (or

color), the features occurring in an image in terms of the object or type of land cover

these features actually represent on the ground (Lillesand and Kiefer, 1994).

Digital image classification uses the spectral information represented by the digital

numbers in one or more spectral bands, and attempts to classify each individual pixel

based on this spectral information as shown in Figure 2.9, but analyst by human

attempting to classify features in an image uses the elements of visual interpretation to

identify homogeneous groups of pixels which represent various features or land cover

classes of interest(Lillesand and Kiefer, 1994).

Figure 2.9: Concept of classification of remotely rensed data

17

Main classification methods are supervised classification and unsupervised

classification.

2.3.4.1 Supervised Classification

The analyst identifies in the imagery homogeneous representative samples of the

different surface cover types (information classes) of interest. In this method, human

identify examples of the information classes (i.e., land cover type) of interest in the

image, these samples are referred to as "training sites" as shown in Figure 2.10 . The

image processing software system is then used to develop a statistical characterization

of the reflectance for each information class. This stage is often called "signature

analysis" and may involve developing a characterization as simple as the mean or the

range of reflectance on each bands, or as complex as detailed analyses of the mean,

variances and covariance over all bands. (Eastman, 1995).

Thus, in a supervised classification operator are first identifying the information classes

which are then used to determine the spectral classes which represent them.

Figure 2.10: Steps in supervised classification

18

2.3.4.2 Unsupervised Classification

Unsupervised classification in essence reverses the supervised classification process.

Spectral classes are grouped first, based solely on the numerical information in the data,

and are then matched by the analyst to information classes (if possible) and does not

require analyst-specified training data. The algorithm used in programs,

called clustering algorithms, are used to determine the natural (statistical) groupings or

structures in the data as shown in Figure 2.11 (Eastman, 1995).

Figure 2.11: Unsupervised classification flow diagram

SEPARATE DATA INTO

GROUPS WITH

CLUSTERING

CLASSIFY DATA INTO

GROUPS

ASSIGN NAME TO EACH

GROUP

SATISFACTORY ?

YES

NO

IMAGE

19

2.3.5 Available Feature Extraction Methods

The term ‘feature’ refers to remote sensing scene objects (e.g. vegetation types,

building, roads, etc.) with similar characteristics (whether they are spectral, spatial or

otherwise). Therefore, the main objective of a feature extraction technique is to

accurately retrieve these features. The term “Feature Extraction” can thus be taken to

encompass a very broad range of techniques and processes. The definition can also be

taken to involve manual , semi-automated and automated vector feature digitizing

(ERDAS, 2013).

Many methods are used for the vectorizing process and feature extraction .Digitizing is

one of a way of conversion of information from analogously produced graphical maps

to machine readable vector or raster formats. Manual and automated methods are

adopted in this study to extract feature from imagery (Stanley, 2003).

2.3.5.1 Manual Digitizing

The simplest way to create vectors from raster layers is to digitize vector objects

manually straight off a computer screen using a mouse or digitizing cursor.

There are two methods of manual digitizing point mode and Stream mode. Both

methods involve the operator moving the cursor on features to be collected. The

difference in the two modes lies in the procedure of collecting those features. (Douglas

and Peucker, 1973; Burroughs, 1986) . The operator manually traces all the lines from

his hardcopy map and creates identical digital map on the computer. It is very time

consuming and level of accuracy is also not excellent.

Almost all programs of GIS can be digitized images using editor toolbar which

available drawing tools (line , polygon , point), some of this programs (ArcGIS,

Geomedia, AutoCAD Map 3D, AutoCAD Raster Design, etc.)

20

2.3.5.2 Automatic Digitizing

Raster and vector are the two basic data structures for storing and manipulating images

and graphics data on a computer. All of the major GIS and CAD software packages

available today are primarily based on one of the two structures, either raster based or

vector based, while they have some extended functions to support other data structures.

Automatic digitizing or so called automated raster to vector conversion, traces lines

automatically from the scanned raster image using image processing and pattern

recognition technique. In this technique, computer traces all the lines which results in

high speed and accuracy along with improved quality of images.

Because of the importance of automated feature extraction from raster to vector

process and the difficulties involved, it has been a major research focus during the past

two decades. Only in recent years, automated raster to vector conversion software on

PCs and small computers become familiar, practical and commercially available for

data acquisition applications.(Yecheng, 1996)

Two methods are used to extract feature from images; one of them is traditional

classification methods are all pixel-based and do not utilize the spatial and context

information of an object and its surroundings, which has potential to further enhance

digital image classification.

The second one is object based feature extraction which is a new method that is widely

used recently. Object based image analysis approach is the approach to image analysis

combining spectral information and spatial information, so with object base approach

not only the spectral information in the image will be used as classification information,

the texture and context information in the image will be combined into classification as

well (Flanders et. al, 2003).

This study reviews and compares between the two methods for feature extraction of

case study images. Figure 2.12 shows the methodology used, which include previous

steps in data preprocessing and image classification (using pixel based and object based

feature extraction).

21

Figure 2.12: Methodology of pixel-based and object based processing

Geometric correction Radiometric correction

Pre-processing

Object Base

Classification

Pixel Base

Classification

Supervised

Classification

Computing Attributes

Image Segmentation

Merging Segments

Processing

Image

Canny Edge Operator

Feature extraction

Gaussian Filtering

Edge Detection and

Linking

22

Before applying automatic feature extraction methods, suitable correction for images

should be applied, the second is step using basics image processing operation to obtain

high contrast between objects and background after applying some of statistical

operations which can reduce noise in the image, which is of benefit to the feature

extraction techniques to be considered later. As such, these basic operations are usually

for preprocessing for later feature extraction or to improve display quality. Typical

image enhancement technique include gray scale conversion ,histogram conversion and

color composition, etc (Nixon and Aguado, 2008).

Image enhancement techniques can be divided into two broad categories:

- Spatial domain methods, which operate directly on pixels.

- frequency domain methods, which operate on the Fourier transform of an image.

Unfortunately, there is no general theory for determining what is `good' image

enhancement when it comes to human perception.

- Histogram

Histogram is spatial domain method and identify and determine it of image is very

important, so the intensity histogram shows how individual brightness levels are

occupied in an image; the image contrast is measured by the range of brightness levels.

The histogram plots the number of pixels with a particular brightness level against the

brightness level. The histogram can be evaluated by the operator histogram, in Code

2.1. The operator first initializes the histogram to zero. Then, the operator works by

counting up the number of image points that have an intensity at a particular value

(Nixon and Aguado, 2008).

Code 2.1: Evaluating the histogram

23

- Thresholding

Thresholding is the simplest method of image segmentation. This operator selects

pixels that have a particular value, or are within a specified range. It can be used to find

objects within a picture if their brightness level (or range) is known and its important

element to edge detection of feature from images. The lower the threshold, the more

edges will be detected, and the result will be increasingly susceptible to noise and

detecting edges of irrelevant features in the image. Conversely a high threshold may

miss subtle edges, or result in fragmented edges (Nixon and Aguado, 2008).

There are more advanced techniques, known as optimal thresholding. These usually

seek to select a value for the threshold that separates an object from its background.

This suggests that the object has a different range of intensities to the background, in

order that an appropriate threshold can be chosen as illustrated in Figure 2.13. Otsu’s

method (Otsu, 1979) is one of the most popular techniques of optimal thresholding

(Nixon and Aguado, 2008). The basis is use of the normalized histogram where the

number of points at each level is divided by the total number of points in the image. As

such, this represents a probability distribution for the intensity levels as :

𝑝(𝑙) =𝑁(𝑙)𝑁2 𝐸𝑞. 1

This can be used to compute the zero- and first-order cumulative moments of the

normalized histogram up to the k th level as

ω(k) = �𝑝(𝑙)𝑘

𝑙=1

𝐸𝑞. 2

and

µ(k) = �𝑙 .𝑝(𝑙) 𝐸𝑞. 3𝑘

𝑙=1

The total mean level of the image is given by

24

µ T = � 𝑙 .𝑝(𝑙)𝑁𝑚𝑎𝑥

𝑙=1 Eq.4

The variance of the class separability is then the ratio

σ𝐵 2 (𝑘) =

�𝜇𝑇.𝜔(𝑘) − 𝜇(𝑘)�2

𝜔(𝑘)�1 − 𝜔(𝑘)� ∀𝑘 ∈ 1,𝑁𝑚𝑎𝑥 𝐸𝑞. 5

The optimal threshold is the level for which the variance of class separability is at its

maximum, namely the optimal threshold Topt is that for which the variance

σ𝐵 2 �𝑇𝑜𝑝𝑡� = 𝑚𝑎𝑥1≤𝐾≤𝑁𝑚𝑎𝑥(σ𝐵

2 (𝐾)) Eq.6

Figure 2.13: Optimal thresholding

The code implementing Otsu’s technique is given in Code 2.2, which followed by

Figure 2.14 which describes the effect optimal, higher and lower value of thersolding on

edge detection (Nixon and Aguado, 2008).

25

Code 2.2: Optimal thresholding by Otsu’s technique

Figure 2.14: Comparison between edge detection at different thresholding levels

26

2.3.5.2.1 Object-Based Feature Extraction

The object based classification concept is that important semantic information necessary

to interpret an image which not represented in single pixels, but in meaningful image

objects and their mutual relations as shown in Figure 2.15. Image analysis is based on

contiguous, homogeneous image regions that are generated by initial image

segmentation. Connecting all the regions, the image content is represented as a network

of image objects. These image objects act as the building blocks for the subsequent

image analysis (Yoon et. al, 2004).

Figure 2.15: Concept of object-based feature extraction

The workflow of object based feature extraction involves the following steps:

- Dividing an image into segments.

- Computing various attributes for the segments.

- Creating several new classes.

- Interactively assigning segments (called training samples) to each class.

- Classifying the entire image with a K Nearest Neighbor (KNN), Support Vector

Machine (SVM), or Principal Components Analysis (PCA) supervised

classification method, based on your training samples.

- Exporting the classes to a shapefile or classification image.

27

2.3.5.2.2 76BPixel-Based Feature Extraction

Traditional remote sensing classification techniques are pixel-based, meaning that

spectral information in each pixel is used to classify imagery as shown in Figure 2.16.

This technique works well with hyperspectral data, but it is not ideal for panchromatic

or multispectral imagery (Yoon et. al, 2004).

Figure 2.16: Concept of pixel-based feature extraction

A conventional pixel-based classification approach, based on statistical algorithms has

been used for decades (Tso and Mather 2009). Generally, this approach is very useful in

large scale images where a separation can be efficiently established between water,

urban, and vegetation areas according to their spectral characteristics. However, in cases

of similar spectral information the ability of this approach is limited (Yan 2003).

The main assumption in using this approach is that the single pixel contains sufficient

grey level information to be assigned to a certain class. The challenge in using this

approach, especially in residential areas many objects such as; buildings, concrete roads,

sidewalks and parking lots will have a nearly identical spectral response as the main

construction material is almost the same. The unsupervised classification approach is

often more suitable in an automatic classification solution, where user interference is

not required. The primary difference between the unsupervised and supervised

approaches is that for the unsupervised methods, only the number of clusters are entered

without selecting any training data set, and the classifier automatically constructs the

clusters by minimizing a predefined error function (Yiu-ming 2005).

28

Many approaches to image interpretation are based on edges, since analysis based on

edge detection is insensitive to change in the overall illumination level, edge detection

highlights image contrast. There are many edge detection methods such as Sobel,

Prewitt, Roberts, Canny. This section discussed canny edge detector (Nixon and

Aguado, 2008).

-Canny edge detector

The Canny edge detection operator (Canny, 1986) is perhaps the most popular edge

detection technique at present. It was formulated with three main objectives:

• optimal detection with no spurious responses.

• good localization with minimal distance between detected and true edge

position.

• single response to eliminate multiple responses to a single edge.

Canny edge detection is an optimal method for step edges’ detection in the spatial

domain. Canny uses three criteria to design his edge detector. First, a reliable detection

of edges with low probability of missing true edges, and a low probability of detecting

false edges must be achieved. Second, the detected edges should have a minimum

distance to the true location along the edge. Third, there should be only one response to

a single edge (thin lines for edges), Figure 2.17 shows the type of step edges (Nixon and

Aguado, 2008).

Figure 2.17: Type of step edges

Depending on these criteria, the Canny edge detector first reduce the response to noises,

this can be effected by optimal smoothing, then it finds the image gradient to highlight

regions with high derivatives. The regions in image with high derivatives are tracked by

the algorithm to suppress any pixel that is not at the maximum (non-maximum

suppression). The remaining pixels are further reduced by two thresholds T1 and T2. If

29

the magnitude is below T1, that’s mean none edge so it is set to zero, if the magnitude is

above T2, it is made an edge. If the magnitude is between the two thresholds, then it is

set to zero unless there is a path from this pixel to a pixel with a gradient above T2

(Canny, 1986).

Image edge extraction using the Canny Operator consists of six steps as shown in

workflow in Figure 2.18.

Figure 2.18: Steps of edge extraction using the Canny operator

Gaussian filtering

Computation of gradients

Edge pixel classification

Non-maxima suppression

Subpixel estimation

Edge tracking and

thinning

Feature extraction

Case Study Image

Can

ny O

pera

tor

30

In order to implement the canny edge detector algorithm, a series of steps must be

followed.

Step 1: Gaussian Filtering

The first step of canny edge detection is to filter image any noise in the original image

before trying to locate and detect any edges. The Gaussian filter is used to blur and

remove unwanted detail and noise.

The Gaussian operator has been considered to be optimal for image smoothing. The

template for the Gaussian operator has values set by the Gaussian relationship. The

Gaussian function g at coordinates x, y is controlled by the variance σ2 according to:

𝑔(𝑥,𝑦,𝜎) = 1

2𝜋𝜎2𝑒−�

𝑥2+𝑦22𝜎2 � 𝐸𝑞. 7

The 3×3 operator (Figure 2.19a) retains many more of the features than those retained

by direct averaging. The effect of larger size is to remove more noise at the expense of

losing small features in images as is clear in 5×5 and the 7×7 operators in Figure 2.19

(b) and (c), respectively (Nixon and Aguado, 2008).

Figure 2.19: Applying Gaussian averaging

31

Step 2: Gradient calculation

After smoothing the image and eliminating the noise, the next step is to find the edge

strength by taking the gradient of the image –there are many ways and masks to perform

the gradient calculation such as simple derivative, derivative of Gaussian and Slobel

operator. Canny algorithm basically finds edges where the gray scale intensity of the

image changes the most. These areas are found by determining gradients of the image.

Gradients at each pixel in the smoothed image are determined by applying what is

known as the Sobel-operator.

When finding edges, looking for the steepest descent as well as the steepest ascent since

both represent a high change in the intensity of the image. Figure 2.20 depicts the

gradient and orientation process (Rangarajan, 2002).

Figure 2.20: Illustration of gradient calculation in Canny operator

The gradient value for each pixel we can get the magnitude of the gradient by:

�∇𝐼𝑥,𝑦� = �(𝑑𝑥𝑥,𝑦 )2 + (𝑑𝑦𝑥,𝑦 )2 𝐸𝑞. 8

The main purpose of doing this is to highlight regions with high spatial derivatives. The

orientation of the edge can be determined by the next equation:

𝜃 = arctan �𝑑𝑦𝑥,𝑦

𝑑𝑥𝑥,𝑦� Eq.9

32

The Sobel function (Code 2.3) convolves the generalized Sobel template with the

picture supplied as argument, to give outputs which are the images of edge magnitude

and direction, in vector form (Nixon and Aguado, 2008).

Code 2.3: Generalized Sobel operator

Step 3: Edge pixel classification

The lengths of the gradient vector computed previously are compared to a threshold.

Pixels with a gradient vector length smaller than that threshold are classified as

homogeneous pixels, whereas pixels with a gradient vector length larger than that

threshold are classified as edge pixels.

Once the edge classified and direction is known from orientation equation, the next step

is to relate the edge direction to a direction that can be traced in an image. So if the

pixels of a 5x5 image are aligned as follows:

x x x x x x x x x x x x a x x x x x x x x x x x x

Then, it can be seen by looking at pixel "a", there are only four possible directions when

describing the surrounding pixels ,in the horizontal direction (0 degrees) , along the

positive diagonal (45 degrees) , in the vertical direction (90 degrees), or 135 degrees

33

(along the negative diagonal). So the edge orientation has to be resolved into one of

these four directions depending on which direction it is closest to (e.g. if the orientation

angle is found to be 3 degrees, make it zero degrees). Think of this as taking a

semicircle and dividing it into 5 regions as shown in Figure 2.21 (Rangarajan, 2002).

Figure 2.21: Semicircle edge orientation

Therefore, any edge direction falling within the yellow range (0 to 22.5 & 157.5 to 180

degrees) is set to 0 degrees. Any edge direction falling in the green range (22.5 to 67.5

degrees) is set to 45 degrees. Any edge direction falling in the blue range (67.5 to 112.5

degrees) is set to 90 degrees. And finally, any edge direction falling within the red range

(112.5 to 157.5 degrees) is set to 135 degrees (Rangarajan, 2002).

Step 4: Non-Maximal Suppression

This step works with the magnitude and orientation of the gradient of the pixel under

consideration and creates one pixel-width edge and locates the highest points in the

edge magnitude data. For all pixels classified as edge pixels, their gradient vector

lengths are compared to the lengths of the two neighboring edge pixels in gradient

direction. If any of these two neighbours has a larger gradient length, the current pixel is

not a local maximum of gradient vector length, and thus the pixel is declared not to be

an edge pixel. Thus, only the most significant edge pixels remain as shown in Figure

2.22 (Rangarajan, 2002).

34

Figure 2.22: Non maximal suppression procedure

The non-maximum suppression operator, non_max in Code 2.4

Code 2.4: Non-maximum suppression

35

Step 5: Subpixel estimation

For the remaining edge pixels, a subpixel position of the actual edge point is carried out.

The gradients in gradient direction are approximated by a second order polynomial, and

the position of the edge point is estimated as the position of the maximum of the

polynomial.

Step 6: Edge tracking

Neighbouring edge pixels are connected by an edge tracking algorithm, which results in

edge pixel chains. These edge pixel chains will be split at junctions (i.e. where edges

intersect) and at points of high curvature. After that, short edge pixel chains will be

discarded. The Minimum Line Length in [pixels] can be selected in the according

numerical field. Each edge pixel chain is approximated by a polygon in an iterative

procedure: Splitting, Approximation and Merging which defined as edge thinning

(Rangarajan, 2002).

2.4 Conclusion

After reviewing the pervious subjects of remote sensing and image processing to extract

feature from imagery using available feature extraction methods such as manual and

automatic, it is clear that should be use image pre-processing before extract feature from

imagery to enhance resolution. Pixel based feature extraction based on single pixel and

use statistical algorithms but Object based feature extraction based on contiguous,

homogeneous image regions.

36

3 CHAPTER 3: LITERATURE REVIEW

3.1 Scope

This chapter gives a background of historical stages for feature extraction at the

beginning of imagery were acquired by humans, and provides an overview of the types

of imagery being used for feature extraction. This chapter also describes the methods

and techniques used for feature extraction and the quantitative and qualitative accuracy

assessment of these procedures..

3.2 Background

Since the first photographic images were acquired the humans have sought to extract

information and data from imagery. As early as the mid nineteenth century the French

Army Corps of Engineers experimented with using aerial photographs for

reconnaissance and mapping (Wolf and Dewitt, 2000). With rapid development in

technology the expansion and improvement of photogrammetry and remote sensing

techniques stimulated by advances such as the development of color film, the invention

of the airplane, and unceasing improvement in instrumentation and techniques (Wolf

and Dewitt, 2000).

On the other hand, interest in feature extraction field has increased significantly since

the advent of digital imagery and the possibilities associated with electronic processing.

Many of papers and researches with focused conferences provide an overview of many

of the techniques available in theses filed (Baltsavias, et al., 2001; Gruen, et al., 1997;

Gruen, et al., 1995). Other companies such as CRCSI (2011), Erdas Imagine

(Intergraph) (2013), Definiens (2003) and Visual Learning Systems (VLS, 2003) and

others are developing software specifically targeted at feature extraction with various

accuracy and speed .

Technology has improved and commercial access to imagery has continued to expand

and restriction to get of these images became low, so the research into automated

feature extraction from imagery increased . The first panchromatic imagery following

the launch of the first SPOT satellite in 1986 used from Destival (1986) to described the

improvements in feature extraction that were expected using 10 meter resolution. In

moderate resolution imagery with high sensors, such as SPOT, Geo eye, Landsat

37

Thematic Mapper (TM) and IKONOS, linear features such as roads are often narrower

and more accurate and clear than the spatial resolution of the satellite. Many problems

founded in imagery after captured ,so Hemmer (1996) described the problem in pixel of

images as one of the confusing factors in extracting features using imagery from the

satellite sensors available at that time. Wang and Zhang (2000) compared SPOT and

Landsat TM imagery with high spatial resolution aerial photography for extracting road

networks and found that the success of linear feature extraction with more accurate was

particularly related to spatial resolution of images. Their experimentation found that

photography out-performed the lower resolution satellite imagery when extracting roads

in an urban environment. High spatial resolution imagery provides more details

representation of features such as building and road networks and others elements on

maps which needed for many applications that cannot be obtained from lower resolution

image sources (Xiong, 2001). Roads networks appear as curvilinear structures and

building appear as overlapping areas in lower resolution imagery, while in higher

resolution imagery roads and building appear as homogenous regions that satisfy certain

shape or size constraints (Hinz, et al., 2001).

On the other hand, many of the techniques developed for line feature detection search

for roads networks in high spatial resolution imagery as pairs of edges: such techniques

are unsuitable to processing lower resolution imagery.

A common objective of feature extraction is to facilitate the rapid update of GIS data.

(Bonnefon, et al., 2002). Manual digitizing method of data depends on heavily of

human labor, pushing up the cost of developing such databases (Xiong, 2001). An

important factor in developing feature extraction methods and techniques is reduction

the time in creating and updating databases. The Canada Centre for Mapping (CCM)

traditionally updated maps through visual interpretation of imagery (Manual method)

(O’Brien, 1989). The Digitization of maps at the CCM led to a logical move from

manual updates to extraction of information directly from digital sources. Improving the

quality ,consistency and increase of speed derived data from imagery may be a further

reason for using and developing automated procedures.

38

3.3 Types of Imagery Used in Feature Extraction

Feature extraction techniques are applied to imagery with a wide range of spectral and

spatial characteristics. Many authors have reported use of panchromatic aerial imagery

for extracting linear and polygon features as (e.g., Agouris, et al., 2001a; Katartzis, et

al., 2001; Couloigner and Ranchin, 2000; Trinder and Wang, 1998). Also researcher has

used of radar data for extracting linear features (e.g., Hellwich, et al., 2002; Chanussot,

et al., 1999; Tupin, et al., 1998; Iisaka and Sakurai-Amano, 1995), while others such as

Liang, Shiahn and Ming (2007) extracted feature from lidar data; additionally, authors

such as Pelletier (1985) describe feature extraction in agricultural region using of

thermal imagery. Multispectral image sources in the visible or near-infrared portion of

the spectrum such as SPOT was used from several authors to applied feature extraction

techniques (e.g., Nguyen, 2012 and Hui, 2001), Landsat TM (Nguyen, 2012; Wang and

Zhang, 2000), and IKONOS imagery (Lee and Shan, 2012; Gibson, 2003; Dial, et al.,

2001).

Research has also been performed for linear feature extraction from hyperspectral

imagery. (Gardner, et al., 2001). Penn and Livo (2002) reported some success in

extracting road locations from AVIRIS imagery, while Doucette, et al. (1999)

experimented with HYDICE imagery.

Many of advanced sensors provided high resolution of imagery such as GeoEye-1 has

1.65 meter multispectral and 0.5 meter panchromatic, IKONOS has four meter

multispectral and one meter panchromatic; QuickBird imagery has 2.44 meter

multispectral and 0.61 meter panchromatic. With availability of these images much of

the research reported for feature extraction applies to single band, high spatial

resolution imagery. Ikonos (Space Imaging, Inc., 2003) and Quickbird (Digital Globe,

2003) are examples of the new generation of high spatial resolution satellite based

sensors. These sensors record their highest spatial resolution in a panchromatic mode.

Processing techniques that aim to use the highest spatial resolution data sources will

need to extract and exploit the spatial and/or contextual information with a limited

number of spectral channels (Guindon, 1999).

39

3.4 Feature Extraction

3.4.1 Manual Versus Automated Extraction

The simplest methods of feature extraction is manually using visual digitizing of

imagery so humans and computers have complementary strengths: humans are good at

digitizing large areas and recognizing objects which depend on vision optical, whereas

computers are good at optimization of images, detailed delineation and repetition

(McKeown, et al., 1996). Whether automated, manual or a combination of the two

methods, feature extraction can be a very involved process. Manual feature extraction

method needed to investment skills of the operator but can be time consuming and thus

expensive to perform (Baumgartner, et al., 1999). With rapidly changing society and

growing body of digital images archived, the efficient revision of cartographic databases

using GIS implies some form of automated feature extraction (O’Brien, 1989).

Humans by manual digitizing have the ability to digitize and group simple features,

such as points and lines, into meaningful structures . Semi-automated approaches

depend on user provided cues to delineate feature components (Agouris, et al., 2001).

Only a short number of years ago considered fully automatic methods for feature

extraction to be “far out of reach.” (Gruen and Li 1997)

Data collection is often the most expensive component in a GIS application and using

techniques developed that can alleviate this (Ansoult, et al., 1990; Firestone, et al.,

1996). Automated methods offer for consumed time and labor savings and potentially

may improve consistency and accuracy data extracted. Using of automated or semi-

automated methods can also provide cost savings by reducing the training time of photo

interpreters (Pigeon, et al., 1999). Also have other less tangible benefits such as

reducing operator fatigue.

With the availability of high spatial resolution imagery, it is often possible to consider

spatial patterns to a greater degree when looking for specific features on maps. For

example, it is possible to use structural information about roads networks (such as,

linearity, width,…etc.) to distinguish them from other features that may be spectrally

similar. Most of automated or semi-automated feature extraction procedures try to

40

simulate the human interpretation process by incorporating both spectral and spatial

information.

On the other hand, the authors and researchers believe that using a semi-automated in

digitizing imagery approach is optimal method because the ability of humans to

identification objects almost flawlessly with limited effort such as Baumgartner, et al.

(1999) found that their automatic road extraction was not absolutely reliable and

generally required a human operator to edit the results. Humans by optical vision are

able to understand shapes of objects in noisy data and adapt to varying conditions,

without being told explicitly what to expect. The significant challenge is writing

computer code (Algorithms) to simulate this ability.

3.4.2 A Feature Model

Automated feature extraction requires that such a model be defined in a manner that can

be implemented by computer (Trinder and Wang, 1998). Model based processing

exploits the constraints and relationships that define objects, for example, the size,

shape, and material of a building, or the width, material, and direction of a road. The

feature model includes information relating to a range of characteristics such as

intensity, shape, texture, and context (Suetens, et al., 1992). Models are often

characterized as being either flexible or rigid. A rigid model defines features

specifically, for example outlining the allowable size, shape and spectral response. A

flexible model may include specifications in terms of generic constraints, such as

smoothness, rectilinearity, curvature, compactness, symmetry, and homogeneity. An

objective function is used to find a best fit between the model and the image data. Some

techniques use hierarchical types of models (Suetens, et al., 1992). For example, Yee

(1987) identified bridges by first finding potential road segments, then restricting the

search to select those with water on either side.

41

3.5 Techniques for Feature Extraction

There are a lot of techniques were used in recent years to detect and extract linear and

polygon features from imagery. Many of the authors and researchers wrote papers to

review developed extraction techniques for locating a specific feature class in imagery,

such as roads, building and agricultural area (Fischler, et al., 1981).

For example the techniques for linear feature extraction from imagery often divide into

three primary steps: edge detection or road finding, road tracking, and vectorization or

road linking (Park, et al., 2002; Trinder and Wang, 1998). While the rectangle

extraction and detection methods are mainly based on grouping of primitives, like edges

or segments or matching the predefined parametric models.( Kailasrao, et al., 2012).

The level of automation feature extraction techniques varied significantly from human

intervention or not. Some of procedures use a few initial assumptions, such as the

relative brightness of road and building pixels or linearity, and allow the computer to do

the rest.

Many of the techniques reported in the literature combine strategies from a variety of

approaches. Categorizing such approaches becomes a challenge. The following sections

present many of the different techniques (Algorithms) used in automatically extracting

feature from imagery, recognizing that there is often substantial overlap between the

procedures.

3.5.1 Mathematical Morphology

The techniques of mathematical morphology (MM) have proven useful in automating

feature extraction. Mathematical morphology is a theory which provides a number of

useful tools for image analysis, so it has been widely used in digital image processing

and focuses on the area that studies the geometric properties of objects in the images.

This allows the extraction of image components that are useful in the representation and

description of the shape of a region, such as borders and skeletons (Gonzales and

Woods, 2000).

Daryal and Kumar (2010) used mathematical morphology to extract lines developed in

the software MATLAB. Frigato (2008) also used mathematical morphology to carry

out the semi-automatic extraction of linear features. Castro and Centeno (2010) used

42

mathematical morphology to extract lines from Advanced Land Observing Satellite

(ALOS) images. Dong (1997) used mathematical morphology to extract linear features

from gray scale aerial imagery.

In mathematical morphology, images are filtered using a kernel. The output of the

filtering process depends on the match between the image and the kernel and the

operation being performed (O’Brien, 1989). The two basic operations of mathematical

morphology are dilation and erosion (Serra, 1986). The simplest example of

mathematical morphology considers the analysis of binary images. The kernel typically

used for binary imagery is a 3 × 3 array consisting of 0s, or1s. Dilation and erosion can

be performed on binary images using with the kernel as shown in Figure 3.1 (Ali,

2010).

Figure 3.1: Combining erosion and dilation to produce an opening or a closing

3.5.2 Hough Transform

Hough transform is an feature extraction technique used in image analysis, computer

vision, and digital image processing. (Shapiro, 2001). It's an automatic analysis

technique used for detection of linear features in a variety of applications (Karnielli, et

al., 1996). The classical Hough transform was concerned with the identification

of lines in the image, but later the Hough transform has been extended to identifying

positions of arbitrary shapes. The HT uses a parametric approach to describe features of

interest and can detect any feature that can be parameterized (Fitton and Cox, 1998). In

the parameter space, image patterns produce local extremes at the most likely parameter

values (Suetens, et al., 1992).

43

Successful detection of linear features using the HT requires preprocessing to threshold

the input image into a binary layer. Benefits of the HT are that it detects lines with some

fragmentation and it is reasonably unaffected by random noise (Fitton and Cox, 1998).

Turker and San (2010) used Hough Transform to extraction building from high

resolution satellite images. Lohani and Singh (2007) used and developed Hough

Transform for extraction building from LiDAR (Light Detection And Ranging Data).

Lee and Moon (2002) used the parameterization of the HT described by Richard

Duda and Peter Hart (1972) for extracting linear features. Stylianidis and patias (2000)

used Hough Transform in line extraction.

Karnielli, et al. (1996) used the HT to detect linear geological features using three

different image sources: digitized terrestrial photography, digitized airborne

photography, and Landsat TM Imagery.

In this transform image space (x, y) is transformed to a (ρ, θ) parameter space. An

example illustrating this parameterization is shown in Figure 3.2. Based on the example

shown in Figure 3.2, the point (x, y) can be represented in polar coordinates as (r, α).

That is: x = r cos α and y = r sin α. The following can also be observed from Figure 3.2:

ρ = r cos β = r cos (θ - α) = r cos θ cos α + r sin θ sin α

= (x/cos α) cos θ cos α + (y/sin α) sin θ sin α = x cos θ + y sin θ Eq.3.1

Figure 3.2: Illustration of parameters of hough transform

44

3.5.3 Multi-Resolution Techniques

The appearance of roads in digital imagery is dependent on the spectral and radiometric

characteristics of the sensor and the spatial resolution of the imagery. In lower spatial

resolution imagery, roads tend to appear as lines as compared to higher resolution

imagery (less than two meter GSD) where roads appear as elongated homogeneous

regions with consistent width (Baumgartner, et al., 1999). Many authors (e.g.,Wang, et

al. 2005; Gibson, 2003; Couloigner and Ranchin, 2000; Baumgartner, et al., 1999;

Daida and Vesecky, 1991; Trinder and Wang, 1998; Shneier, 1982) report multi-

resolution based approaches to extract roads from imagery.

Many multi-resolution approaches generate lower resolution imagery by degrading a

high-resolution source. Both Shneier (1982) and Baumgartner, et al. (1999) extracted

lines in imagery with reduced resolution, then used this information to identify the roads

in the higher resolution imagery. The image degradation often involves generating an

image pyramid. Shneier (1982) created a pyramid of images by successively passing a 2

× 2 filter over the image and replacing the four-pixel neighborhood with the median

value. As an alternative, Couloigner and Ranchin (2000) used a wavelet transform to

generate pyramid layers. Instead of degrading a high-resolution dataset for multi-

resolution analysis, some authors use multiple image types. Bonnefon, et al. (2002) used

SPOT imagery to approximately identify linear features then used this preliminary data

to identify roads in IKONOS imagery.

Baumgartner, et al. (1999) used a texture- based segmentation procedure to subdivide

the imagery into three regions (urban, rural, and forest) and developed local road

models to suit each region. In rural areas more than 95 percent of roads extracted were

actually roads and 80 to 90 percent of roads were extracted. This approach was less

successful in urban areas, with a visual assessment showing that the fragmented roads in

the built-up area were a challenge for the automated processing algorithm.

3.5.4 Template Matching

Template matching is a technique in digital image processing for finding small parts of

an image which match a template image.(Brunelli, 2009). In this approach, a template

describing the general characteristics of the feature of interest is defined. Templates are

often fixed in terms of attributes such as size, shape, and intensity. Features are

45

extracted by moving the template through the image and evaluating the match at each

location using a similarity measure (e.g., correlation) to find optimal locations (Suetens,

et al., 1992) and match is done on a pixel-by-pixel basis as shown in Figure 3.3. While

the science behind Feature Analyst® (VLSI, 2003), an extension to ESRI ARCGIS®

and ERDAS IMAGINE® software, remains proprietary, the machine learning approach

implemented utilizes user-defined templates when searching for specific features.

Figure 3.3: Template matching technique

Such templates incorporate both spatial and spectral information. Lina et al; (2008) used

template matching with angular texture signature to extract road from high resolution

imagery. Opitz (2002) evaluated Feature Analyst for extracting roads in pan-sharpened

Ikonos imagery and he found that the automated tool provided accurate results with a

substantial reduction in labor. Rak and Kim (2001) developed semi – automatic road

extraction algorithm from IKONOS images using template matching. Poz (2001)

presented an edge following technique that used a local template to define roads in

imagery with a 2 meter pixel size. Poz (2001) evaluated edges by comparing image

regions to rotated templates of width equal to the road. The technique required seed

points from the operator and relied on a well-defined road model. The road tracing

method was visually determined to be successful in the relatively simple test case

presented by Poz (2001).

3.5.5 Dynamic Programming

The term dynamic programming was originally used in the 1940s by Richard

Bellman to describe the process of solving problems where one needs to find the best

decisions one after another. Dynamic programming is a technique for solving

optimization problems when not all variables in the evaluation function are interrelated

simultaneously (Ballard and Brown, 1982). It is a solution strategy for combined

46

optimization problems which involve a sequential decision-making process. It is an

optimization process, expressed as a recursive search (Bellman and Dreyfus, 1962).

This approach is applicable only if a function can be expressed in terms of relationships

between neighboring pixels alone and involves a sequential decision making process

(Gruen and Li, 1997). Gruen and Li (1997) introduced an application , dynamic

programming was used to optimize line feature extraction such as road in SPOT

imagery based on the procedure summarized below:

• Define a curve as a polygon with n vertices.

• Develop a merit function based on several of the radiometric and geometric properties

of the road model (as described in an earlier section e.g., roads are bright, smooth, linear

features, with little change in intensity over a short distance), with limitations on road

curvature forming a fixed constraint.

• Move each vertex around in a window (for example, 5 × 5) to find the maximum of

the merit function based on the characteristics described above.

Poz and Vale (2003) used dynamic programming for semi-automated road extraction

from medium and high resolution images. Bonnefon, et al. (2002) also applied dynamic

programming to find an optimal solution when using SPOT imagery to update existing

GIS data layers.

3.5.6 Particle Swarm Optimization

Particle swarm optimization (PSO) is a swarm intelligence based algorithm to find a

solution to an optimization problem in a search space, or model and predict social

behavior in the presence of objectives. It is a kind of swarm intelligence that is based on

social-psychological principles and provides insights into social behavior, as well as

contributing to engineering applications. The particle swarm optimization algorithm

was first described in 1995 by James Kennedy and Russell C. Eberhart.

Kundra et al. (2010) developed algorithm which based on global threshold (average)

using PSO to detect and extraction objects from images. Setayesh et al. (2010) gave

new contribution to object detection is application of particle swarm optimization for

extraction of geometric properties of an object in an image for accurate recognition

47

especially in noisy environments the edges and the corners of an object are detected by

particle swarm optimization algorithm and then the object is classified based on number

of the corners and attributes of the edges by a simple fuzzy rule-based classifier. Yang

et al. (2006) used PSO to road extraction from (Synthetic-Aperture Radar) SAR Images.

Firstly, manually select the road’s extremities. Secondly, calculate the each pixel's road

membership value using local road detector in the original SAR images. Thirdly, with

particle swarm optimization that is one of the most powerful methods for optimization

problem we obtain the optimal B-spline control points from the result of road detection.

3.5.7 Pixel Swapping

Iisaka and Sakurai-Amano (1995) describe a feature detection approach combining

spectral and spatial information. In traditional spectral analysis, the intensity of a pixel

(ai) is defined simply by the location of the pixel (xi, yi), that is, spatial relationships

between pixels are generally not considered. Iisaka and Sakurai-Amano (1995)

described a spectral relationship as a mapping between two ordered sets.

3.5.8 LSB Snake

LSB-Snakes derive their name from the fact that they are a combination of least squares

template matching (Grain, 1985) and B-spline Snakes. B-spline Snakes have been

applied to satellite and aerial images as shown in Figure 3.4 (Trinder and Li, 1995; Li,

1997) and are an alternative to the polygonal curves. For LSB-Snakes we use three

types of observations, which are also based on the generic road model. These

observations can be divided in two classes, photometric observations, that represent the

gray level matching of images with the object model, and geometric observations that

express the geometric constraints and the a priori knowledge of the location and shape

of the feature to be extracted. Li and Gruen (1996) used LSB snake to linear feature

extraction from multiple images. Li and Gruen (1995) developed automation of Road

Extraction from Space and Aerial Images using LSB snake method.

A visual evaluation performed by Gruen and Li (1997) showed that the LSB-SNAKES

technique was successful even when faced with varying road width, and partial-

occlusion caused by buildings, trees and cars.

48

Figure 3.4: An LSB-snake of a road segment

3.5.9 Edge Detection

Edge detection is the name for a set of mathematical methods which aim at identifying

points in a digital image at which the image brightness changes sharply or, more

formally, has discontinuities. There are many methods for edge detection, but most of

them can be grouped into two categories, search-based and zero-crossing based.

The search-based methods such as (Sobel, Prewitt, Roberts, Canny) as shown in Figure

3.5 detect edges by first computing a measure of edge strength, usually a first-order

derivative expression such as the gradient magnitude, and then searching for local

directional maxima of the gradient magnitude using a computed estimate of the local

orientation of the edge, usually the gradient direction. The zero-crossing based methods

search for zero crossings in a second-order derivative expression computed from the

image in order to find edges. (Gonzalez, 2002)

By far the most well-known edge extraction method is the Canny edge detector.

Canny’s approach was to determine the “optimal” edge detection method for a certain

set of assumptions. Is in fact an approximation to the optimal detector for a step edge

under Gaussian noise.

The three stated objectives of Canny Edge Detection are:

1. Good Detection: There should be a low probability of both false negatives and false

positives.

2. Good Localization: The detected edge points should be close to the true edge.

(a) Initial position (b) Final solution

49

3. Single Response: In contrast to the Laplacian, each image edge should generate only

a single output edge.

Subash, (2011) extracted road networks automatically from satellite images using

extended kalman filtering and efficient particle filtering and the system used canny edge

detection to find roads edge. San and Turker, (2010) extracted building from high

resolution satellite image using Hough transform and after detecting the building

patches, their edges are detected using the Canny edge detection algorithm. Xiao,

(2007) applied the Canny operator to the panchromatic images for edge detection to

extraction building from very high resolution satellite imagery to generate 3D city

model.

Figure 3.5: Type of edge detection algorthiems

3.6 Knowledge Integration

Knowledge engineering is the task of converting knowledge, which may be intuitive,

into some exploitable form (Pigeon, et al., 1999). Combining rules from a variety of

sources, including human intuition, can be challenging. Some commercial software

vendors now provide tools that support integrating rules from a variety of image and

ancillary data sources, for example Expert Classifier©, a component of ERDAS

IMAGINE® (Leica, 2003). Fischler, et al. (1981) used a knowledge integration

approach to locate roads in imagery. Fischler, et al. (1981) used rules to establish a

numerical score for each pixel to indicate the likelihood of that pixel lying on a road

(low values represented a greater probability). The road location was determined by

finding the lowest cost route that satisfied all imposed constraints, such as continuity.

This process can combine a variety of local image feature operators (such as edge

detectors) and additional constraint layers in order to optimize the search for roads.

50

Scores from different layers are combined based on knowledge, for example, about the

responsiveness of the operators used and the applicability to a particular scene.

3.7 Classification-based Feature Extraction

A number of articles specifically take advantage of the multispectral nature of sensors

such as Landsat TM, SPOT and IKONOS to extract road information. Classification is

useful as a preprocessing step in feature extraction, for example, to segment images and

focus a model on particular cover types. Classification techniques have also been

directly applied to solving the problem of linear feature extraction. In most cases, even

with hyperspectral datasets, the spectral information alone was not sufficient to define

roads and the classification was one component of a multistage process. Gardner, et al.

(2001) found that classification of roads using AVIRIS imagery was challenging

because of the similarities of construction materials in roads and roofs. They found that

following the classification with the spatial pattern recognition technique of a Q-tree

filter improved the result. In some applications, a road network is simply one

component of an output product.

3.8 Assessing Feature Extraction Techniques

Most of the papers reviewed relied heavily on visual assessment for reporting the

success of the feature extraction algorithm. For example, Yee (1987) visually compared

road extraction using two different automated methods to roads extracted manually,

reporting only that the manual identification results were comparable to that of the

automated procedure. Baumgartner, et al. (1999) compared roads automatically

identified in several test images with manually plotted reference data to report errors of

omission and commission in applying their road extracting techniques. Baumgartner, et

al. (1999) also reported the relative geometric accuracy for correctly identified roads,

comparing the distance in pixels between visually identified road locations and those

that had been extracted automatically. Such assessments are limited by the accuracy of

the manual road identification. Authors such as Agouris, et al. (2001) experimented

with synthetic images in order to evaluate the validity of their algorithm. For the papers

that did report accuracy statistics, the most commonly reported measures were total

correct, errors of commission and errors of omission. Some authors (e.g., Fischler, et al.,

1981) also defined performance criteria to evaluate their extraction technique.

51

Many authors state that automated feature extraction procedures provide significant

benefits in terms of saving operator time and effort. In spite of this, few of the studies

reviewed stated results to support this assertion.

3.9 Conclusion

Many authors list a host of reasons for automating feature extraction, ranging from time,

cost and energy savings, to product improvements, such as, increased detail or accuracy.

Unfortunately, many of the papers reviewed did not provide results to support these

claims. Many authors stated intentions of locating points in the field for verification of

absolute position, but did not include accuracy statistics for the study reported.

52

4 CHAPTER 4: METHODS EVALUATION

4.1 Scope

This chapter contains detailed description of the steps of the methodology of the

research. It includes steps strategy beginning from data collection of imagery and

image processing of these image to using an existing of automatic feature extraction

methods and methods evaluation.

4.2 Data Collection and Preparation

Maps have been the main source of data for geographic analysis for many years. Raster

data is commonly obtained by scanning maps or collecting aerial photographs and

satellite images.

4.2.1 Data Collection

There are a lot of free sources that offer free aerial photos and satellite images in the

internet as USGS (U.S. Geological Survey) and NASA website but these images with

low resolution in conflict zones as Palestine and from other sources as Gaza

municipality. After many of trials to get suitable aerial and satellite images from various

sources to apply feature extraction methods and comparison between results. Some of

criteria are applied to select a case study such as :diversity of features such as

(buildings, trees, roads, green area), number of features, spatial resolution, contrasting

colors, simplicity and complexity of image.

Three case studies are used to extract building from aerial and satellite images:

- Study area (1) : The study area (1) is an agricultural area in middle area of Gaza

Strip is clipped from aerial photographs (2007) with (0.5 x 0.5 m) spatial

resolution and its coordinates is (34.290 N , 31.260 E). This image contains

number of roads, buildings and agricultural pools as shown in Figure 4.1.

53

Figure 4.1: Study Area (1)

- Study Area (2) : The study area (2) is a residential area of Gaza City, specifically

in the Islamic University of Gaza campus. This image is from an aerial

photograph (2010) with (0.125 x 0.125 m) spatial resolution and has coordinates

(34.430 N , 31.510 E) as shown in Figure 4.2.

Figure 4.2: Study Area (2)

- Study Area (3): is a residential area in the United States of America with (0.38 x

0.38 m) spatial resolution as shown in Figure 4.3.

54

Figure 4.3: Study Area (3)

4.2.2 Image Pre-Processing

Preprocessing of image data often will include radiometric correction and geometric

correction. The following subsections illustrate all needed steps of automatic feature

extraction based on Study Area (2) sample shown in Figure (4.2).

4.2.2.1 Geometric Correction

To correct the geometric distortions, one should apply two steps, geo-referencing and

resampling using ARCGIS 10.1 or Erdas 2013 as shown in Figure (4.4).

The geographic space of each dataset was referenced according to four known

coordinates corresponding to the minimum x and y values, the minimum x and

maximum y values, the maximum x and minimum y values, and the maximum x and y

values. Georeferencing is the process of assigning geographic information to an

image. Knowing where an image is located in the world allows information about

features contained in that image to be determined. This information includes location,

size and distance.

55

Figure 4.4: Geo-referencing method & toolbar in ARCGIS 10.1

After correcting the coordinate system, the spatial characteristics of pixels may be

changed. So resampling should be applied to obtain a new image more pronounced in

which all pixels are correctly positioned within the terrain coordinate system to more

accurate feature extraction methods.

4.2.2.2 Radiometric Correction

Radiometric correction involves the processing of digital images to improve the fidelity

of the brightness value magnitudes. Any image contains radiometric errors and

inconsistencies will be referred to as "noise" these errors should be corrected before the

post processing enhancement, extraction and analysis of information from the image.(

Stow, 2001)

The sources of radiometric noise and the appropriate types of radiometric corrections,

partially depend on the sensor and mode of imaging used to capture the digital image

data such as aerial photography, optical scanners, sensors and others.

Improvement quality of images which is used in Study Area (2), radiometric noise

reduction, is performed using ERDAS 2013 as shown in Figure 4.5.

56

Figure 4.5: Noise reduction of Study Area (2)

4.2.2.3 Image Enhancement

Figure 4.6 shows each study area and its corresponding histogram. From this histogram,

Image appearance can be enhanced better before extraction process and can also reveal

whether there is much noise in the image, if the ideal histogram is known.

Figure 4.6: Histogram of study areas

57

Feature Extraction is a combined process of segmenting an image into regions of pixels,

computing attributes for each region to create objects, and classifying the objects to

extract it.

4.3 Feature Extraction Methods

Digitizing is a way of conversion of information from analogously produced graphical

maps to machine readable vector or raster formats. Many methods are used for the

vectorizing process and feature extraction. Manual and automated methods are adopted

in this study to extract features from imagery (Stanley, 2003). Figure 4.7 shows the

methods and programs which are used in this study.

Figure 4.7: Feature extraction methods and programs

4.3.1 Manual Digitizing

Almost all programs of GIS can digitize images using editor toolbar with available

drawing tools (line , polygon , point). The first step in digitizing a map requires

creating feature class into personal geodatabse and adjust all configurations of feature

class such as coordinate system, xy tolerance and add fields database using Arc- catalog

program as shown in Figure 4.8.

Manual Automated

Feature Extraction

Methods

Pixel-Based Object-Based

Barista 2.3.1 Erdas Objective 2013

ENVI 5.0

ArcGIS 10.1

58

Figure 4.8: Create feature class in personal geodatabase using Arc-catalge

To start digitizing, the user should use Arc-map window and add feature class with

active editor toolbar as shown in Figure 4.9, then zoom to specific areas on screen and

trace points, lines, or polygons on the map. Because the maps are already in the correct

geographic coordinate system anything digitized on top of the map will also be in the

correct coordinate system.

Figure 4.9: Editor toolbar in Arc-map

Manual digitizing depends on the human visual, focus and ability of the operator to

tracking and digitizing edges of the feature on the suitable location of pixel to get more

accurate. So, the manual feature extraction consume a lot of time as shown in section

59

4.4. Figure 4.10 shows the steps of the manual tracking and digitizing edges of the

building on the map and Figure 4.11 shows manual feature extraction from study areas.

Figure 4.10: Manual tracking and digitizing edges of the buildings

Figure 4.11: Manual feature extraction of study areas

Before

Before

Before

After

After

After

60

There are some advantages and disadvantages of manual feature extraction:

Advantages:

- Can be performed on inexpensive equipment.

- Doesn’t need high quality maps.

- Requires little training.

Disadvantages:

- Tedious.

- Time consuming.

4.3.2 Automatic Digitizing

Two methods are used to extract feature from images; one of them is traditional

classification methods, are all pixel-based, do not utilize the spatial and context

information of an object and its surroundings, which has potential to further enhance

digital image classification. The second one is Object based feature extraction which is

a new method that is widely used recently.

4.3.2.1 Object-Based Feature Extraction

Commercial programs are introduced with new tools and developed new algorithms to

extract feature from images such as (ERDAS Imagine 2013, ENVI 5.0, Feature analyst

5.2, Feature extraction 11, FETEX 2.0). The processing that applied to the case study

image using two programs (ENVI 5.0 and ERDAS imagine objective 2013).

A. ENVI 5.0

ENVI® (the Environment for Visualizing Images) is a revolutionary image processing

system. From its inception, ENVI was designed to address the numerous, specific needs

of those who regularly use satellite and aircraft remote sensing data.

ENVI feature extraction consists a combined process of segmenting an image into

regions of pixels, then computing attributes for each region to create objects. The

workflow consists of two primary steps as shown in Figure 4.12: find objects and

extract features. The find objects task is divided into four steps: segment, merge, refine,

and compute attributes. When you complete this task, you will perform the extract

61

features task, which consists of supervised or rule-based classification and exporting

classification results to shapefiles and/ or raster images.

Figure 4.12: Feature extraction workflow of ENVI 5.0

In ENVI 5.0, open map and run rule based feature extraction toolbox as shown in Figure

4.13 to start change setting of processing as follow :

- Image Segmentation

Image segmentation is the primary technique that is used to convert a scene or image

into multiple objects. Applying the object-based paradigm to image analysis refers to

analyzing the image in object space rather than in pixel space, and objects can be used

as the primitives for image classification rather than pixels, so image segmentation is

the process of partition an image into segments by grouping neighboring pixels with

similar feature values (brightness, texture, color, etc.)

62

Image segmentation can be performed automatically by employing an edge-based

segmentation algorithm that is very fast, familiar end user and only requires one input

parameter (scale level). Adjust the scale level as necessary, values range from 0.0 (finest

segmentation) to 100 (coarsest segmentation; all pixels are assigned to one segment).

Figure 4.13: Object based feature extraction toolbox

Figure 4.14 shows detect boundary of (Tayba building in Islamic University of Gaza)

using edge algorithm at different levels of segmentation.

Figure 4.14: Image segmentation result at different levels

63

- Merging Segments

Some of features on the image are larger, textured areas such as trees and building.

Merging Segments used to aggregate small segments within these areas where over-

segmentation may be have a problem. Scale level for merging is a useful option for

improving the delineation of roads, buildings and farms boundaries as it is clearly

shown in Figure 4.15.

Figure 4.15: Merging segments result at different levels

To get the best results, we must try to change scale level in segment setting algorithm

and merge setting algorithm at the same time as shown in Figure 4.16.

Figure 4.16: Optimal segmentation level 82 and merge level 95

64

- Supervised Classification

The classification procedure starts with an image segmentation based on the single

intensity band. After segmentation and merge segments, a supervised classification is

performed, that using samples for the different classes (buildings, roads, and farms).

The classifier used is a K nearest neighborhood classifier that defines set of classes

which can be separated automatically. This method is generally more robust than a

traditional nearest-neighbor classifier, since the K nearest distances are used as a

majority vote to determine which class the target belongs to. The K Nearest Neighbor

method is much less sensitive to outliers, noise in the dataset and generally produces a

more accurate classification result compared with traditional nearest-neighbor methods.

Figure 4.17 shows the building class that performed in object base feature extraction

and shape file exported as vector .

Figure 4.17: Classfication method and building extraction

By using the K Nearest Neighbor classification method, an unambiguous classification

result can generally be achieved quite quickly, resulting in improved processing

efficiency, especially in a large projects where manual editing tends to seriously reduce

productivity, and a more sophisticated end product.

K nearest neighbor classification method Shape file exported

65

B. ERDAS IMAGINE OJECTIVE 2013

ERDAS IMAGINE, the world’s leading geospatial data authoring system, incorporates

geospatial image processing and analysis, remote sensing, GIS capabilities into a

powerful and convenient package.

IMAGINE Objective in ERDAS 2013 version and versions released later is one of the

software solutions to object-oriented classification and feature extraction available in

the market, which includes an innovative set of tools, enabling geospatial data layers to

be created and maintained using remotely sensed imagery. Combining inferential

learning with expert knowledge in a true object-oriented feature extraction environment,

IMAGINE Objective emulates human visual processing. IMAGINE Objective also

encapsulates vector processing operators to produce GIS-ready data with minimal post

processing.

Pixels are the primitive informational elements in raster imagery and the starting point

of classifications and extraction process. Classifying pixels depends on training pixels

which are identified by the user with training polygons in the imagery. During the

training phase pixels that are representative of the feature of interest may be submitted

to compute Pixel Cue Metrics to train the Pixel Classifier. The overall process flow of

extraction features using imagine objective is shown in Figure 4.18.

Figure 4.18: Erdas objective process work flow diagram

66

The basic structure of a feature model showing the linear manner in which the data is

analyzed. Operators are designed as plugins so that can be more easily added as

required for specific feature extraction scenarios, so after start imagine objective should

be add and change the parameter to get the best results. The steps of feature extraction

as follow:

- Add new variable button then select and add raster.

- All configuration and parameter of detect objects and extract features add from

left tree view menu as shown in Figure 4.19.

Figure 4.19: Configurtions tree view menu of imagine objective

- Raster pixel processor (RPP)

Among the functions available in the software to perform classification are; Normalized

Difference Vegetation Index (NDVI), Single Feature Probability (SFP), shadow and

texture. In this study, SFP and shadow function was used to extract the buildings from

images. The definition of training areas for individual buildings as well as for

background pixels is of central importance to the outcome.

67

Training areas had been chosen carefully not include any background pixel. During the

training phase, pixels that are representative of the individual buildings, were submitted

to compute pixel cue metrics to train the pixel classifier. Pick several rooftops with

varying shades to get samples representing the range of colors among the building

rooftops as shown in Figure 4.20, then check automatically extract background pixels so

that the SFP classifier will automatically attempt to extract background samples from

the outside of your training samples.

Figure 4.20: Training areas of imagine objective

- Raster object creators (ROC)

In this step, the function ‘segmentation’ was applied which performed Segment an

image into geometric primitives. While it's not necessarily derived from the Pixel

Probability Layer, the raster object segments will have the zonal mean pixel

probabilities as attributes as shown in Figure 22b below. Apply edge Detection should

be checked on to computing the optimal thresholding level as shown in Figure 4.21.

68

Figure 4.21: Comparison between edge detection at different thresholding levels

-Raster object operators (ROO)

Probability filter and size filter allow to keep pixel objects with high probability and a

certain amount of pixels only. Size filter was filtered out raster objects that are too small

or too large thus allowing one to restrict the set of raster objects to those of an

appropriate size of individual buildings. Filtering out objects improved efficiency of the

model, since fewer objects are processed in later stages of the model. The output was a

new raster object layer as shown in Figure 4.22c below.

-Raster to vector conversion (RVC)

Output from the step (ROC) to step (ROO) contained pixels that were grouped as raster

objects which had associated probability metrics. With polygon trace raster objects were

automatically vectorized converting objects from the raster domain to the vector domain

as shown in Figure 4.22d below. It takes as input the ROO and converts each raster

object into a vector object as polygon then produces a vector object layer. The following

steps were applied on vectorized objects.

69

-Vector object operators (VOO)

This processed the geometric features of the vector objects and stored the probability

value for each feature of each object in the attribute table. In this step the vector objects

were smoothened in shape which accelerates later processing as shown in Figure 4.22e

below.

-Vector object processor (VOP)

This performed classification on the tree vector objects from VOO above which

involved specifying shape and area cues. This was used by object classifier to measure

shape and size property of the building objects and uses the cues to assign a probability

to each object in the group of building vector objects.

-Vector cleanup operator (VCO)

These classes of operators are performed on the vector object layer output from the

object classifier query and are intended for probability thresholding and for cleanup of

the object polygon or polyline footprint. The finish step is Orthogonality to adjust an

object’s polygon to be comprised of all straight lines and right angles (used for

buildings) as shown in Figure 4.22f.

70

Figure 4.22: Steps of feature extarction using Erdas ojective 2013

Figure 4.22 shows the pervious steps of building extraction from image. To get the best

results, we build many of scenarios and applied more than 30 trials of change

parameters and configurations.

71

4.3.2.2 Pixel-Based Feature Extraction

Many programs are still pixel based to extract feature from images, although they are

old techniques. These programs depend on many of edge detection algorithms such as

(Canny, Sobel, Prewitt, Roberts and others techniques).

This section uses Barista 2.3.1 program to detect and extract features from images,

Barista software was developed for the processing of high-resolution satellite imagery

for the purpose of 3D geopositioning, 3D information extraction, orthoimage generation

and automated edge extraction. This software was developed in Geomatics Department

at University of Melbourne in Australia.

The feature extraction dialog provides a user interface for selecting methods for image

feature extraction and the parameters for these feature extraction methods. Extraction

features process as follows:

1- Import images which should be in (tiff, jpg ,ecw, jpeg, tif ) format.

2- Select region to extract feature from it ,then right click to select extract features

as shown in Figure 4.23.

Figure 4.23: Extract features using Barista program

3- Feature extraction dialog provides a user interface for selecting methods for

image feature extraction and the parameters for these feature extraction methods.

In the group feature extraction mode, two methods for feature extraction can be

selected as shown in Figure 4.24.

72

Canny: select this option if you want to extract image edges using the Canny

operator.

Foerstner Operator: Select this option if you want to extract image points

and/or edges using the framework for polymorphic feature extraction that is

often referred to as the Foerstner operator. Different parameters will be

accessible, depending any methods selected.

Figure 4.24: Feature extraction dialog of Barista program

Pixel-based feature extraction method fails in differentiating between road and

buildings. The boundaries between frames and trees are not very clear as shown in

Figure 4.25. Canny feature extraction mode is the best pixel based extraction method

and its results is excellent.

(a) Canny operator (b) Foerstner operator

73

Figure 4.25: Feature Extraction using Canny Operator

4.4 Results and Discussion

After applying and testing some of available feature extraction techniques, results of

each method is shown in Table 4.1. Six criteria are used to evaluate and compare

between results of feature extracted from images such as (assessment accuracy, time

spent, cost, end user friendly, image resolution, hardware requirements).

In order to evaluate and compare the accuracy of the feature extraction results created

by the two approaches, pixel-based and object-based, some of buildings has been

surveyed in study area (2) such as Tayba and Al cafateria buildings in the Islamic

University of Gaza campus as shown in Figure 4.26, and some of buildings in study

area (1) to compare area and final shape building extracted.

Figure 4.26: Surveying layout of Tayba building

74

Table 4.1: Results of feature extraction methods

Manual Method Automated Method

Object-Based Pixel-Based

Programs ArcGIS 10.1 Erdas Objective 2013 ENVI 5.0 Barista 2.3.1

Study Area (1)

Study Area (2)

Study Area (3)

75

The comparison results between area of extracted sample of buildings are shown in

Table 4.2. Note that the pixel-based feature extraction method fails to differentiate

between roads and buildings. The boundaries between frames and trees are not very

clear, also areas of buildings are inaccurate. On the other hand, it shows buildings, trees

and shadow classes very well.

While results of object based feature extraction is very close to the accurate and shows

more clear boundaries between objects. Buildings are clearly selected as objects in the

image. All the features are illustrated with almost exact shape as it is in the ground.

Farms, trees, roads and shadows are all can be clearly seen that no more mix classified.

The margin of error and the difference in the areas either increase or decrease a few, this

indicates that programs depend on object based technology are used new powerful

classification and object detection algorithms. IMAGINE Objective 2013 includes an

innovative set of tools for feature extraction, update and change detection, enabling

geospatial data layers to be created and maintained through the use of remotely sensed

imagery, but dealing with Erdas imagine objective needs more experience.

Envi 5.0 program is very easy to use for extraction of features from images by easy

steps and end user friendly wizard with more accurate results.

76

Table 4.2: Comparison between area of extracted sample of buildings

Methods

Surveying

layout Manual

Automated

Object-Based Pixel-Based

Programs ArcGIS 10.1 Erdas 2013 ENVI 5.0 Barista 2.3

Study Area

(1)

Building ID (5) /Area (m2)

310 306 307 321 252

Building ID (2) /Area (m2)

320 321 339 322 273

Study Area

(2)

Tayba building / Area (m2)

1500 1545 1430 1596 1312

Cafateria building / Area (m2)

750 804 804 806 593

Study Area

(3)

Building ID (21) /Area (m2)

Not available 241.33 246.17 257.4 195

Building ID (11) /Area (m2)

Not available 204.66 235.94 215.13 170

From the characteristics of the two extraction methods, in object oriented image

analysis, object is not a single pixel takes part in the classification. Properly performed

segmentation creates good image objects that facilities the extraction from the image.

Traditional feature extraction methods (manual) process depends heavily on human

labor, which makes GIS database development an expensive and time-consuming

operation when performed manually.

77

Table 4.3 shows the manual method needs are more time to digitize all objects in image,

where need to 20 minutes using manual digitizing method if compared with a minute

using automatic feature extraction.

Table 4.3: Time spent to extract features from images by different methods

Methods Manual

Automated

Object-Based Pixel-Based

Programs ArcGIS 10.1 Erdas 2013 ENVI 5.0 Barista 2.3

Time spent (Minutes)

Study Area (1) 7 / 13

buildings

1 1 1

Study Area (2) 21/ 5

buildings

1 1 1

Study Area (3) 20 / 30

buildings

1 1 1

Comparison of the result of the accuracy assessment shows that object oriented image

analysis attain higher overall accuracy (94%) compared with (82%) for Pixel-based

classification approach depending on area and shape criteria as shown in Table 4.3.

78

Table 4.4: Summary of overall comparison between feature extraction methods

Methods Manual

Automated

Object-Based Pixel-Based

Programs ArcGIS 10.1 Erdas 2013 ENVI 5.0 Barista 2.3

Assessment

accuracy

97 % 93 % 94 % 82 %

Time

consuming

(Minutes)

24 1 1 1

End user

friendly

Yes Yes Yes Yes

Cost More

specialists

One

specialist

One

specialist

One

specialist

High image

resolution

Necessary Necessary Necessary Necessary

Hardware

specifications

Moderate High High Moderate

Finally, object based feature extraction method is more accurate than of pixel based

feature extraction method, where manual digitizing gives excellent results but it

consume more time and effort.

We use in this research the famous of feature extraction programs, such as Erdas 2013

and Envi 2013, It is founded that Envi program is very easy to use for non-specialist

human and the results is accurate, but process of feature extraction need computers with

high specification.

79

5 CHAPTER 5: CONCLUSION AND RECOMMENDATIONS

5.1 Conclusion

- Different techniques for feature extraction are deeply discussed.

- Three case study samples are selected to extract buildings, samples were

selected strictly within the specific criteria and different degrees of difficulty.

- Comprehensive comparison is made between those different techniques either

manual ones or automatic (pixel based and object based).

- The study shows that the object based feature extraction method is more

accurate than of pixel based feature extraction method where manual digitizing

gives excellent results but it consume more time and effort.

5.2 Recommendations

Recommend therefore:

- Developing object-based data models that can be used to identify remote sensed

data for more accurate feature extraction.

- Cooperation with local and global teams in automatic feature extraction

techniques.

- Continue working on the development or improvement of existing algorithms to

enhance the percentage of accuracy and speed of data.

- Urged all Gaza municipalities and GIS users to use automatic feature extraction

from images, because it saves time, money and effort.

- Supporting researches and projects in this field from Palestinian universities.

- Work to provide high image resolution to gives more accurate in automatic

feature extraction techniques.

80

REFERENCES

Aggarwal, S., 2003. Principles of remote sensing. Photogrammetry and Remote Sensing Division, Indian Institute of Remote Sensing, Dehra Dun.

Agouris, P. Stefanidis, A. and Gyftakis, S., 2001. Differential snakes for change detection in road segments, Photogrammetric Engineering & Remote Sensing, 67(12):1391–1399.

Ali, H., 2010. Digital images edge detection using mathematical morphology operations. Iraqi Journal of Science, Department of Physics, College of Science, University of Baghdad. Baghdad, Iraq.

Baltsavias, E.P. Gruen, A. and Van Gool, L., 2001. Automated Extraction of Man-Made Objects from Aerial and Space Images (III), A. A. Balkema Publishers, Lisse, The Netherlands, 415 p.

Baumgartner, A. Steger, C. Mayer, H. Eckstein, W. and Ebner, H., 1999. Automatic road extraction based on multi-scale, grouping, and context, Photogrammetric Engineering & Remote Sensing, 65(7): 777–785.

Bokhary, M., 2009. Comparison between Pixel Based Classification and Object Base Feature Extraction Approaches For a Very High Resolution Remote Sensed Image. Jeddah, Saudi Arabia.

Bonnefon, R. Dhérété, P. and Desachy, J., 2002. Geographic information system updating using remote sensing images, Pattern Recognition Letters, 23(9):1073–1083.

Castro, P. and Centeno, J., 2010. road extraction from alos images using mathematical morphology. ISPRS TC VII Symposium – 100 Years ISPRS, Vienna, Austria, July 5–7, 2010, IAPRS, Vol. XXXVIII, Part 7B.

Colwell, R.N., 1983. Manual of Remote Sensing. Second Edition. Vol I: Theory, Instruments and Techniques. American Society of Photogrammetry and Remote Sensing ASPRS, Falls Church.

Couloigner, I., and Ranchin, T., 2000. Mapping of urban areas: A multiresolution modeling approach for semi-automatic extraction of streets, Photogrammetric Engineering & Remote Sensing, 66(7): 867–874.

Curran, P.J., 1985. Principles of Remote Sensing. Longman Group Limited, London.

Daida, J.M., and Vesecky, J.F., 1991. Mathematical morphology for automated analysis of remotely sensed objects, Proceedings of International Geoscience and Remote Sensing Symposium, 03–06 June, Espoo, Finland, pp. 1795–1799.

Daryal, N. and Kumar, V., 2010. Linear Extraction of Satellite Imageries using Mathematical Morphology. International Journal of Computer Applications (0975 – 8887).

81

Destival, I., 1986. Mathematical morphology applied to remote sensing, Acta Astronautica, 13(6/7):371–385.

Eastman, J.R., 1995. IDRIS for Windows, User's Guide, Clark University, Worcester, MA, USA, 405pp

Firestone, L. Rupert, S. Olsen, J. and Mueller, W., 1996. Automated feature extraction: The key to future productivity, Highlight Article in Photogrammetric Engineering & Remote Sensing, 62(6): 671–674.

Fischler, M.A. Tenenbaum, J.M. and Wolf, H.C., 1981. Detection of roads and linear structures in low-resolution aerial imagery using a multisource knowledge integration technique, Computer Graphics and Image Processing, 15:201–233.

Fitton, N.C. and Cox, S.J.D., 1998. Optimising the application of the Hough transform for automatic feature extraction from geoscientific images. Computers and Geosciences, 24(10):933–951.

Flanders, D. Mryka, H. and Joan, P., 2003. Preliminary evaluation of eCognition object-based software for cut block delineation and feature extraction, Can. J. Remote Sensing, 20: 441-452

Frith, M., 1997. Measuring error in manually digitizing maps. Master thesis, Geography department, University of Regina, Redlands, California.

Fua, P. Gruen, A. and Li, H., 1997. Optimization-Based Approaches To Feature Extraction from Aerial Images. Institute of Geodesy and Photogrammetry Swiss Federal Institute of Technology, Switzerland.

Gardner, M.E. Roberts, D.A. Funk, C. and Noronha, V., 2001. Road extraction from AVIRIS using spectral mixture and Q-tree filter techniques, Proceedings of the AVIRIS Airborne Geoscience Workshop, 27 February–March, Pasadena, California.

Gruen, A. Kuebler, O. and Agouris, P., 1995. Automatic Extraction of Man-Made Objects from Aerial and Space Images, Birkhaeuser Verlag, Basel, Switzerland, 320 p.

Gruen, A. and Li, H., 1997. Automation of Road Extraction from Space and Aerial Images. Institute of Gerodesy and photogrammetry, Swiss Federal Institute of Technology, Zurich, Switzerland.

Gruen, A. and Li, H., 1997. Semi-automatic linear feature extraction by dynamic programming and LSB-snakes, Photogrammetric Engineering & Remote Sensing, 63(8):985–995.

Hemmer, T.H., 1996. Towards automation of the extraction of lines of communication from multispectral images using a spatio-spectral extraction technique, Proceedings of the SPIE: Algorithms for Multispectral and Hyperspectral Imagery II, 09–11 April, Orlando Florida, Volume 2758, pp. 115–126.

Hinz, S. Baumgartner, A. Mayer, H. Wiedemann, C. and H. Ebner, 2001. Automated Extraction of Man-Made Objects from Aerial and Space Images (III) (E. Baltsavias, A.

82

Gruen and L. Van Gool, editors), A. A. Balkema Publishers, Lisse, The Netherlands, pp. 255–265.

Iisaka, J. Sakurai-Amano, T. and Lukowski, T.I., 1995. Automated detection of road intersections from ERS-1 SAR imagery, Proceedings of International Geoscience and Remote Sensing Symposium, 10–14 July, Firenze, Italy, pp. 676–678.

Kailasrao, D.N. Husen, S. and Vikram, S., 2012. A Review of Techniques to Recognise and Extract Man-made Objects from Topographic Images. Department of Computer Sciences, Hingoli, (MH), India.

Kundra, H. Panchal, V.K. Arora, S. Singh, K. and Kaura, H., 2010. Extraction of Satellite Image using Particle Swarm Optimization. International Journal of Engineering (IJE), Volume (4): Issue (1).

Lillesand, T.M. and Kiefer, R.W., 1994. Remote Sensing and Image Interpretation. 3rd ed. Wiley, New York.

Lin, X. G. Zhang, J. X. Liu, Z. J. and Shen, J., 2008. semi-automatic extraction of ribbon roads form high resolution remotely sensed imagery by cooperation between angular texture signature and template matching. Chinese Academy of Surveying and Mapping, Beijing, China.

Lohani, B. and Singh, R., 2007. Development of a hough transform based algorithm for extraction of buildings from actual and simulated LiDAR data. Indian Institute of Technology Kanpur, Kanpur, India.

McKeown, Jr., D.M., C. McGlone, S.D. Cochran, Y.C. Hsieh, M. Roux, and J.A. Shufelt, 1996. Feature Extraction and Object Recognition, Manual of Photogrammetry Addendum, American Society for Photogrammetry and Remote Sensing, Bethesda, Maryland, pp. 195–211.

Murayama, Y., 2010. Remote Sensing Image Processing. Graduate School Life and Environment Sciences, University of Tsukuba, Japan.

Natural Resources, 2007. Fundamentals of Remote Sensing, Canada.

Nixon, M. and Aguado, A., 2008. Feature extraction and image Processing. 2rd ed. Academic Press of Elsevier, Linacre House, Jordan Hill, Oxford OX2 8DP, UK.

O’Brien, D., 1989. Road network extraction from SPOT panchromatic data, CISM Journal ACSGC, 43(20):121–126.

Opitz, D.W., 2002. Hierarchical feature extraction: Removing the clutter, Proceedings of the ESRI International User Conference, 08–12 July, San Diego, California.

Penn, B.S. and Livo, K.E., 2002. Using the semi-automated plugin tool for feature identification, recognition and extraction(SPITFIRE) to extract roads and surface material types from AVIRIS imagery, Proceedings of the AVIRIS Airborne Geoscience Workshop, Pasadena, California.

83

Pigeon, L. Solaiman, B. Thomson, K.P.B. Moulin, B. and Toutin, T., 1999. Human-experts rules modeling for linear planimetric features extraction in a remotely sensed images data fusion context, Proceedings of International Geoscience and Remote Sensing Symposium, 28 June–02 July, Hamburg, Germany, pp. 2369–2371.

Poz, A.P. and Vale, G. M., 2003. dynamic programming approach for semi-automated road extraction from medium- and high-resolution images. Dept. of Cartography, Sao Paulo State University, Brazil.

Poz, A.P.D., 2001. Semi-automatic road extraction based on edge and correlation analyses, Proceedings of the ASPRS Annual Conference, 23–27 August, St Louis, Missouri (American Society for Photogrammetry and Remote Sensing).

Quackenbush, L., 2004. A Review of Techniques for Extracting Linear Features from Imagery. Photogrammetric Engineering & Remote Sensing Vol. 70, No. 12, December 2004, pp. 1383–1392.

Sachs, J., 2001. Image Resampling. Digital Light & Color.

Sanderson, R.S, 2001. Introduction to remote sensing. New Mexico State University.

Serra, J., 1986. Introduction to mathematical morphology, Computer Vision, Graphics and Image Processing, 35:283–305.

Setayesh, M. Zhang, M. and Johnston, M., 2010. Feature Extraction and Detection of Simple Objects Using Particle Swarm Optimisation. Victoria University of Wellington, Wellington, New Zealand.

Shneier, M.O., 1982. Extracting linear features from images using pyramids, IEEE Transactions on Systems, Man and Cybernetics, July–Aug:569–572.

Stanley, K., 2003. Digitizing. Virginia university.

Stylianidis, E., 2002. using hough transform in line extraction. The Aristotle University of Thessaloniki, Department of Cadastre Photogrammetry and Cartography, Thessaloniki, Greece.

Suetens, P. Fua, P. and Hanson, A.J., 1992. Computational strategies for object recognition, ACM Computing Surveys, 24(1):5–61.

Trinder, J.C. and Wang, Y., 1998. Automatic road extraction from aerial images, Digital Signal Processing, 8(4):215–224.

Tso, B. and Mather, P.M., 2009. Classification Methods for Remotely Sensed Data, 2rd ed. CRC Press.

Tso, B. and Mather, P.M., 2009. Pattern Recognition Principles T2 - Classification Methods for Remotely Sensed Data, 2rd ed. Classification Methods for Remotely Sensed Data, 2rd ed, Taylor and Francic.

84

Turker, M. and Koc San, D., 2010. building extraction from high resolution satellite images using hough transform. International Archives of the Photogrammetry. Remote Sensing and Spatial Information Science, Volume XXXVIII, Part 8, Kyoto, Japan.

Wang, J. and Zhang, Q., 2000. Applicability of a gradient profile algorithm for road network extraction: Sensor, resolution and background considerations, Canadian Journal of Remote Sensing, 26(5):428–439.

Wang, L. Qin, Q. Du, SH. Chen, D. and Tao, J., 2005. Road Extraction from Remote Sensing Image Based on Multi-resolution Analysis. Institute of Remote Sensing and GIS, Peking University, China.

Wolf, P.R. and Dewitt, B.A., 2000. Elements of Photogrammetry: With Applications in GIS, Third Edition, McGraw Hill, Boston, Massachusetts, 608 p.

Xiong, D., 2001. Automated road network extraction from high resolution images, Technical Notes, U.S. Department of Transportation, National Consortia on Remote Sensing in Transportation, Albuquerque, New Mexico, Issue 3, May, pp. 1–4.

Yan, G., 2003. Pixel based and object-oriented image analysis for coal fire research. Enchede, The Netherlands, International Institute for Geo-Information Science and Earth Observation. Master: 93

Yecheng, W., 1996. Automated Creation of DEM from Contour Maps: Developments in Raster to Vector Conversion Technology, NEARC Conference, Saratoga Spring, NY.

Yee, B., 1987. An expert system for planimetric feature extraction, Proceedings of International Geoscience and Remote Sensing Symposium, 18–21 May, Ann Arbor, MI, pp. 321–325.

Yiu-ming, C., 2005. On rival penalization controlled competitive learning for clustering with automatic cluster number selection. Knowledge and Data Engineering, IEEE Transactions on 17(11): 1583-1588

Yoon, G.W., CHO, S.I., Chae G.J. and Park, J.H., 2004. Automatic LAND Cover Classification of Landsat Images Using Feature Database in a network.

.