fabio scotti - manuel roveri universit à degli studi, milano, italy

U N I V E R S I T À D E G L I S T U D I D I M I L A N O

C17 SC for Environmental Applications and Remote Sensing I M S C I A

Soft Computing for Environmental Applications and Remote Sensing

Soft computing for Remote Sensing Image Processing and

Interpretation

Fabio Scotti - Manuel Roveri

Università degli studi, Milano, Italy



Introduction (I)

• In order to take advantage and make good use of remote sensing data, we must be able to extract meaningful information from the imagery.

• Interpretation and analysis of remote sensing imagery involves the identification and/or measurement of various targets in an image in order to extract useful information about them.

• Soft computing methods can be used in many applications and in many modules of a remote sensing systems (i.e., the design of the system, preprocessing modules, enhancement modules, classification modules)



Introduction (II)

• In this lecture we firstly introduce the basics in image processing, in particular the following techniques:– Preprocessing;– Enhancement;– automatic Classification and Interpretation.

• In the second part of the lesson we will present the main soft-computing techniques used in Remote Sensing and in the environmental applications.



PART A Classical

techniques



The basics of the Remote Sensing Image Processing and Automatic Interpretation (I)

• Our goal is to understand the basic techniques to analyze the RS images, in particular:– Element of visual interpretation;– Basic of Digital Image Processing;– Preprocessing;– Image enhancement;– Image Transformations;– Image Classification, Analysis and Data Integration;

• Please read carefully the tutorial L3_Analysis1.pdf (*) linked in the course page.

(*) Goddard Space Flight Center, NASA



The basics of the Remote Sensing Image Processing and Automatic Interpretation (II)

• Our goals are to understand the first techniques to extract information from RS image, focalizing on an applicative example. Important issues are:– Band Information Characteristics;– False Color View;– True Color View;– Contrast Stretching and Spatial Filtering;– Principal Components Analysis;– Image Ratioing;

• Please read carefully the tutorial L3_Analysis2.pdf (*) linked in the course page. (*) Goddard Space Flight Center, NASA



PART B Soft-computing

techniques



Towards advanced Remote Sensing Image Processing and Automatic Interpretation (III)

• Let’s face the problem of interpretation/classification. Our goals are now to understand:– Unsupervised Classification;– Supervised Classification;– Minimum Distance Classification;– Maximum Likelihood Classification;– Application of a Probabilistic Neural Network Classifier.

• Please read carefully the tutorial L3_Analysis3.pdf (*) linked in the course page. (*) Goddard Space Flight Center, NASA



An overview of Soft Computing methods for Spectral Image Analysis

• Exploitation of the wealth of information in spectral images has yet to match up to the sensors' capabilities, as conventional methods often prove inadequate.

• ANNs hold the promise to revolutionize this area by overcoming many of the mathematical obstacles that traditional techniques fail at.

• By providing high speed when implemented in parallel hardware, (near-)real time processing of extremely high data volumes, typical in remote sensing spectral imaging, will also be possible.

Please read the paper L3_Paper1.pdf

linked in the course page.



Knowledge discovery from multispectral Satellite Images by Fuzzy Neural Networks

• Fuzzy Neural Networks can provide approaches to extract knowledge from multispectral images. For example it is possible to optimize classification rules using fuzzy neural networks.

• The goal of the reading is to understand how the knowledge can be transferred and exploited into the Fuzzy-NN with respect to this application.





A temporally neural adaptive classifier for multispectral imagery

• In this work we can see how a probabilistic neural network (PNN) is devised to account for the changes in the feature space as a result of environmental variations.

• The proposed methodology is used to develop a pixel-based cloud classification system.





Satellite constellation design using genetic algorithm

• The automatic satellite constellation design with satellite diversity and radio resource management is a problem that can be successfully solved using genetic algorithms methods.

• The automatic satellite constellation design means that some parameters of satellite constellation design can be determined simultaneously. The total number of satellites, the altitude of a satellite, the angle between planes, the angle shift between satellites and the inclination angle are considered in the design.

• Satellite constellation design can modeled using a multiobjective genetic algorithm.





End of the lecture

fabio scotti - manuel roveri universit à degli studi, milano, italy

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