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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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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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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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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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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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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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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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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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We start from scanned maps Later
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%3DGhttp://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. 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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