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PRE-PROCESSING IN
IMAGE ANALYSIS OFSATELLITE PICTURES
BEGM KLTR
110010205
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In order to retrieve, manipulate and process rawsatellite images we make use of commercial
computer software, in particular ENVI(ENnvironment for Visualizing Images) written inIDL (Interactive Data Language).ENVI is usedfor data visualization and analysis of satellite
images. With a full understanding of IDL and theuse of key components of the Interactive DataLanguage, we are able to customize, composeand modify algorithms. This allows us to prompt
and direct ENVI to meet our specific needs andtailor, to our needs, the processing of thesatellite data.
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Satellite data comes from the SeaStar, a polarorbiting satellite launched in 1997, which carries
the SeaWiFS (Sea-viewing Wide Field-of-viewSensor) sensor. The SeaStar satellite travels atan altitude of about 1000 km above the Earth. Ittravels pole to pole in ninety-nine minutes.
SeaWiFS is an eight-channel sensor sensingradiation in the range of 0.402-0.885 m m with aswath width of 2800-km. Radiation sensed bythe eight channels of SeaWiFS comes from four
sources: air (gas) scattering and absorption,aerosol scattering and absorption, cloudreflectance and surface reflectance (Fig.1).
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Satellite imagery used in the detectionof change along coastlines is
processed in a standardized fashionto ensure temporal, spatial, andspectral compatibility betweenscenes. Imagery is initially selected tocorrelate as closely as possible with
season and time-of-year coincidentwith high biomass and favorableatmospheric conditions as appropriate
per region.
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Processing Steps
Spot satellites can transmit imagedata to the ground in two ways,depending on whether or not thespacecraft is within range of areceiving station. As the satelliteproceeds along its orbit, foursituations arise concerningimagery acquisition and image
data transmission to ground.
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Processing Steps
The satellite is within range of a Direct Receiving Station(DRS), so imagery can be down-linked in real-timeprovided both satellite and DRS are suitablyprogrammed.
The satellite is not within range of a Spot DRS.Programmed acquisitions are executed and the imagedata stored on the onboard recorders.
The satellite is within range of a main receiving station(Kiruna or Toulouse). It can thus be programmed eitherto down-link image data in real-time or play back the
onboard recorders and transmit image data recordedearlier during the same orbital revolution. The rest of the time, the satellite is on standby ready to
acquire imagery in accordance with uplinked commands.
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Processing Steps
Once data has been transmitted, the SPOTimages undergo preprocessing operations (forSPOT data, the term "processing" is used only interms of data manipulations undertaken by end-users).
The data transmissions are demodulated,synchronized and simultaneously recorded ontotwo high-density data tapes (HDDTs). One of theHDDTs is used as an archive master while theother HDDT acts as a backup for the mastertape.
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Processing Steps
A SPOT satellite data-collection passlasting approximately 10 minutes with aconstant viewing configuration yields two
data segments with each segmentcontaining approximately 75 scenes. Thisyield represents use of either one HRV set
to dual mode or use of both HRVs insingle mode. The size of individual scenesvaries.
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Processing Steps
The SPOT scenes are defined by thefollowing additional preprocessingcharacteristics only when there have been
user requests for the scenes: Preprocessing level
Computer compatible tape (CCT) or film
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Satellite Image Aquisition andPre-processing
Two Landsat-TM images and one ERS-1SAR scene were used in this study. Thesewere already available within the JRC
archive, as they have been used in aprevious study within the EMAP Unit. Assuch, there were no costs involved,
specific to this project, with regard toimage acquisition and pre-processing.
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Image Pre-processing
Preprocessing of satellite images prior toimage classification and change detectionis essential. Preprocessing commonly
comprises a series of sequentialoperations, including atmosphericcorrection or normalization, image
registration, geometric correction, andmasking (e.g., for clouds, water, irrelevantfeatures)
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Image Pre-processing
The normalization of satellite imagery takes into accountthe combined, measurable reflectances of theatmosphere, aerosol scattering and absorption, and theearths surface. It is the volatility of the atmosphere
which can introduce variation between the reflectancevalues or digital numbers (DNs) of satellite imagesacquired at different times. Although the effects of theatmosphere upon remotely sensed data are notconsidered errors, since they are part of the signal
received by the sensing device, consideration of theseeffects is important. The goal conveniently should bethat following image preprocessing, all images shouldappear as if they were acquired from the same sensor.
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Satellite image rectification
The goal of image rectification is to facilitate theoverlay of additional imagery and othergeographic data sets. A standard map area, withboundaries set in UTM, is established for each
scene, thus all image files for the same region,once rectified, will occupy the same map area.The UTM bounds for the scene are establishedaccording to the file size, the 28.5 x 28.5 mpixels, and the minimum/maximum northing and
easting required to contain the full scene area.These boundaries, the UTM zone and theellipsoid are established on each newly- createdempty file.
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Geometric Rectification
Geometric rectification of the imageryresamples or changes the pixel grid to fitthat of a map projection or another
reference image. This becomes especiallyimportant when scene to scenecomparisons of individual pixels in
applications such as change detection arebeing sought.
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Geometric Rectification
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Subset of Study Area
In some cases, Landsat TM scenes are muchlarger than a project study area. In theseinstances it is beneficial to reduce the size of theimage file to include only the area of interest.
This not only eliminates the extraneous data inthe file, but it speeds up processing due to thesmaller amount of data to process. This isimportant when utilizing multiband data such asLandsat TM imagery. This reduction of data is
known as subsetting. This process cuts out thepreferred study area from the image scene intoa smaller more manageable file.
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Subset of Study Area
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Subset of Study Area
A Landsat TM image is 115 miles (185 kilometers) wideby 106 miles (170kilometers) long and has a total area of12,190 square miles or 31,450 square kilometers.
Carroll County has an area of approximately 641 square
miles. In order to subset the study area from each of thefour Landsat scenes, a vector file defining the countyboundary with the same georeferenced coordinates asthe Landsat images, UTM Zone 15, NAD27, wasimported into PCI Imageworks. The county boundary
vector file was converted to a binary bitmap mask andoverlaid on to each of the TM scenes. The county maskacts as a virtual cookie-cutter and subsets the study areasimilar to the previous figure.
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Preprocessing Procedure
Before the creation of the minimumimages, preprocessing must occur. Thepre-processing procedure consists of six
steps: (1) collection; (2) downloading; (3)unzipping twice; (4) executing thepreprocessing algorithms through the
ENVI software; (5) checking the finalpreprocessed images, and (6) executingthe patch procedure, where necessary.
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Preprocessing Procedure
Once this preprocessingprocedure is complete, an
image containing clouds,surface reflectance and
aerosol reflectance is created
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Final preprocessing JPEG image consisting solely of
clouds, surface reflectance and aerosol reflectance.
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Image processing
Once the raw remote sensing digital data hasbeen acquired, it is then processed into usableinformation. Analog film photographs arechemically processed in a darkroom whereas
digital images are processed within a computer.Processing digital data involves changing thedata to correct for certain types of distortions.Whenever data is changed to correct for onetype of distortion, the possibility of the creating
another type of distortion exists. The changesmade to remote sensing data involve two majoroperations: preprocessingand postprocessing.
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Radiometric corrections
Radiometric corrections are made to theraw digital image data to correct forbrightness values, of the object on the
ground, that have been distorted becauseof sensor calibration or sensor malfunctionproblems. The distortion of images iscaused by the scattering of reflected
electromagnetic light energy due to aconstantly changing atmosphere. This isone source of sensor calibration error.
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System correction uses a geographicreference point for a pixel element such asthat provided by the global positioning
system. Correction accuracy often variesdepending upon the accuracy of theposition given by the global positioningsystem. Aircraft platform system instability
is shown in the first figure. Preprocessingcorrection removes the motion distortionas shown in second figure.
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.
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Data Processing, Interpretation andAnalysis
Remote sensing data available in pictorialor digital form need to be interpreted toderive meaningful information. To interpret
the remote sensing data, knowledge of thespectral reflectance signature of variousobjects on the earth is essential. The datacan be interpreted either visually, digitally or
both. Image interpretation and analysis isbeyond the scope of this guide; here wefocus on image processing, enhancement,georeferencing and categorization.
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Data Processing, Interpretation andAnalysis
Before images can be analyzed, somedegree of pre-processing is necessary tocorrect for any distortion inherent in the
images due to the characteristics ofimaging system and conditions.Commonly used pre-processing
procedures include: radiometric correction,geometric correction and atmosphericcorrection.
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Data Processing, Interpretation andAnalysis
Once pre-processing is completed, images canbe enhanced to improve the visual appearanceof the objects on the image. Commonly used
image enhancement techniques include imagereduction, image magnification; transectextraction, contrast adjustments, band ratioing,spatial filtering, Fourier transformations, principal
components analysis, and texture transformationThese are all used to extract useful informationthat assists in image interpretation.
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Data Processing, Interpretation andAnalysis
For both visual image interpretation and digital imageprocessing, the availability of secondary data andknowledge of the analyst are extremely important. Thevisual interpretation can be done using various viewingand interpretation devices. Most commonly used
elements of visual analysis are tone, color, size, shape,texture, pattern, height, shadow, site and association ofthe object under investigation. Digital image processingrelies primarily on the radiance of image pictureelements (pixels) for each band. Radiance is thentranslated into digital numbers (DNs), or gray scaleintensity, for example from 0 (lowest intensity, or black)to 255 (highest intensity, or white). A DN for a specificband will indicate the intensity of the radiance at thatwavelength.
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Data Processing, Interpretation andAnalysis
Georeferencing is the process of taking theimage in its raw format (rows and columns ofdata) and linking it to the land that it covers.
Images are georeferenced by linking spatiallydistributed control points in the satellite image topoints on base maps or points referenced in thefield through global positioning systems. The
raster data in the image is thereby registered toa Cartesian coordinate system, and can becombined with other georeferenced data sets ina geographic information system.
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Data Processing, Interpretation andAnalysis
The output of remote sensing dataanalysis can be presented in a variety ofways including a printout of the enhanced
image itself, an image map, a thematicmap (e.g. land use map), a spatialdatabase, summary statistics and/or
graphs. The output data can be integratedwith a geographic information system(GIS) database for further analysis.
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http://images.google.com.tr/imgres?imgurl=http://www.colellachiara.com/devcon05/Termini%2520Satellite.jpg&imgrefurl=http://www.colellachiara.com/devcon05/rome.html&h=842&w=997&sz=178&tbnid=jFkMD5iTxyTc4M:&tbnh=125&tbnw=149&hl=tr&start=10&prev=/images%3Fq%3D%2Bsatellite%2Bpictures%26svnum%3D10%26hl%3Dtr%26lr%3D%26sa%3DG -
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We start from scanned maps Later
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We start from scanned maps. Laterthis can be extended to satellite
images.
The color channels are decomposed, but instead of RGB,
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The color channels are decomposed, but instead of RGB,CMY is used. From the components we use the yellow
channel, beacuse it is easy to detect sea, which is blue, so
having only a little yellow component.
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In the last step the coastline is detected, using the followingalgorithms:
-Box filtering
-Robert's gradient-Tresholding
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