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Kohonen Maps on Multispectral images

Djelloul Mokadem, Abdelmalek Amine, Zakaria Elberrichi, David Helbert

To cite this version:

Djelloul Mokadem, Abdelmalek Amine, Zakaria Elberrichi, David Helbert. Detection of Urban Areas using Genetic Algorithms and Kohonen Maps on Multispectral images. International Jour- nal of Organizational and Collective Intelligence (IJOCI), IGI Global, 2018, 8 (1), pp.46 - 62.

�10.4018/IJOCI.2018010104�. �hal-01696710�

(2)

DOI: 10.4018/IJOCI.2018010104

Copyright©2018,IGIGlobal.CopyingordistributinginprintorelectronicformswithoutwrittenpermissionofIGIGlobalisprohibited.



Detection of Urban Areas using Genetic Algorithms and Kohonen Maps on Multispectral images

Djelloul Mokadem, GeCoDe Laboratory, Tahar Moulay University of Saida, Algeria

Abdelmalek Amine, GeCoDe Laboratory, Department of Computer Science, Tahar Moulay University of Saida, Algeria Zakaria Elberrichi, Djllali Liabes University of SidiBelAbbes, Algeria

David Helbert, Xlim, CNRS, University of Poitiers, Chasseneuil-Futuroscope, France

ABSTRACT

Inthisarticle,thedetectionofurbanareasonsatellitemultispectralLandsatimages.Thegoalisto

improvethevisualinterpretationsofimagesfromremotesensingexpertswhooftenremainsubjective.

Interpretationsdependdeeplyonthequalityofsegmentationwhichitselfdependsonthequalityof

samples.Aremotesensingexpertmustactuallypreparethesesamples.Toenhancethesegmentation

process,thisarticleproposestousegeneticalgorithmstoevolvetheinitialpopulationofsamples

pickedmanuallyandgetthemostoptimalsamples.ThesesampleswillbeusedtotraintheKohonen

mapsforfurtherclassificationofamultispectralsatelliteimage.Resultsareobtainedbyinjecting

geneticalgorithmsinsamplingphaseandthispaperprovestheeffectivenessoftheproposedapproach.

KeywORDS

Genetic Algorithms, Kohonen Maps, Multispectral Satellite Image, Segmentation, Urban Areas

1. INTRODUCTION

WiththerecentinnovationsinEarthobservationtechniques(sensors,satellites,anddata),“urban”

remotesensingor“remotesensingurbanapplications”haverapidlygainedpopularityamongurban

plannersand,moregenerally,administrationsinchargeofterritorialplanning.

Indeed,remotesensingmakesitpossibletoincreaseourunderstandingofurbanareasindifferent

ways,althoughtherealpotentialofthistechniqueisoftenchallengedbythecomplexityoftheurban

environmentitself.(Xiaojun,2011)

Previousstudieshavebeencarriedoutontheurbantheme,notablytheworkof(Antoine,2009),

hetriedtodevelopamethodabletodetectandqualifychangesinsmallareasfromVeryHighspatial

Resolution(VHR)remotesensingdataacquiredatdifferentdatesandfromdifferentsourcesby

comparingthetexturalpropertiesofobjectsofinterest.First,hetriedtoextractobjectsbyaregion

growingsegmentationandthen,theirtexturearecompared.

Anotherworkhasbeencarriedoutinthisdirection,(Ashish,2010)usedatechniquebasedon

fuzzyclusteringapproachwhichtakescareofspatialcorrelationbetweenneighboringpixelsofthe

differenceimageproducedbycomparingtwoimagesacquiredonthesamegeographicalareaat

differenttimes.

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UsingalwaystheVHRsatellitedata,(Tobias,2017)proposedanovelobject-basedapproach

forunsupervisedchangedetectionwithfocusonindividualbuildings,hefirstappliedaprincipal

componentanalysistogetherwithauniqueproceduretodeterminethenumberofrelevantprincipal

componentsisperformedasapredecessorforchangedetection;thenheusedk-meansclusteringfor

discriminationofchangedandunchangedbuildings.

(Andrew,2017)usedadifferenttechniquecalledImportVectorMachine(IVM)whichbuilds

uponthepopularSupportVectorMachine(SVM)methodology(Ribana,2012).Toobtaintheoptimum

classification,theIVMalgorithmexploresallpossiblesubsetsoftrainingdataforoptimalselection

(termedimportvectors)whicharederivedthroughsuccessivelyaddingtrainingdatasamplesuntil

agivenconvergencecriterionismet(Ribana,2012).Datasamplesareselectedaccordingtotheir

contributiontotheclassificationsolution.

1.1. Other Literature on Image Segmentation and Visualization on Medical Science (Chang,2018)hasusedcomputationalintelligenceandproposedhisMapReduceframeworkwith

fusionalgorithmastechniquesofvisualizationtosimulatemedicalimaginginordertoexploreareas

thatcannotbeeasilyachievedbyconventionalways.

He(Chang,2017)alsouseddataanalyticsandvisualizationtostudyhowcellsandgenescan

becomemalignantandharmfultohumanbodies,theapproachheadoptedconsistinpresentingby

analyticsallcomplexconceptsandsequenceofevents(takingplaceeverysecond)inordertofacilitate

theunderstandingthecomplexbiologicaltransformations.

Onweatherstudies:

AdemonstrationusingCloudComputingtechnologies,MapReduceandoptimizationtechniques

wasexposedby(Chang,2017)tosimulatetemperaturedistributions,analyzeweatherdataand

forecasttemperaturesbasedonstudyingtrendsfromthehistoricaldata.Finally,numericalcomputing

istransformedintovisualizationafterclassifyingresultsintoclustersinordertofacilitatethe

interpretationofoutputresults.

OnSocialnetworks:

In(Chang,2017),theauthorpresentedhisarchitecturefortheSocialNetworkAnalysisPlatform

(SNAP)anddevelopedhisSocialNetworkAPIwithwhich,hepresentedhowtoextractinformation

fromFacebook,whichprocessesBigDataoftheauthor’snetwork.Thedetailsofnetworkhavebeen

discussedandthetechniquestoanalyzeandvisualizedatahavebeenpresented.Thesetechniques

aimtounderstandforexamplethebehaviorofcustomersandthusthelatesttrendinthemarket.

However,inthiswork,wedonotstudytheurbanchangeonmulti-temporalsatelliteimagesasit

ismentionedinpreviousrelatedworks,butwefocusontheproblemofthedetectionofurbanareas

onmultispectralLandsatsatelliteimages(compositeofbands4,3and1).Pickinggoodsamples

requirestheengagementofanexpertinremotesensingimageanalysistogetanoptimalclassification

accordingtoanoptimaltrainingset;thesuburbanspacesareveryoftenveryheterogeneousand

subjecttoabruptandirregularchangesintimeandspace(Antoine,2009).

So,giventhatthequalityoftheclassificationdependscloselyonthequalityofsampling,we

havetheideatoimprovethequantityandthequalityofthesamplesfromaninitialandcrediblegroup

ofsamplesmanuallypickedusingtheconceptofthenaturalevolutionofthespeciesonwhichthe

ideaofthegeneticalgorithms(GAinthispaper)isfounded(Holland,1975).Thesesamplesmostly

fictitiousthatweconsiderasthebestrepresentativesofdifferentclassesaretheninjectedinthe

classificationprocesstotraintheKohonenmaps.TheadoptionofKohonenmapsasaclassification

toolisjustifiedsimplybytheuseofmapsintheself-adaptationofweights,whichultimatelyconverge

toanoptimalconfiguration(Thomas,2001).

TheKohonenmapshaveprovedtheireffectiveness,especiallyincaseofclassificationofweakly

homogeneousimagesinwhichwenoteseveralweaknessessuchasthelackofcontrastandthepresence

ofnoiseandblurringwhichfeaturethedatatobe.

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Thisworkplanalsoreducessignificantlythecomplexityofthedataanalysisfrom7thematic

mapperstoonly3thematicmappersfrompreviousstudiesthatreducedthesizeoftheLandsatdata

spacefrom7to3usingthePrincipleComponentAnalysis(PCA).

Intheendwevalidatetheproposedapproachthatweadoptedbyapplyingtwoknownmeasures

(ValidityMeasure(Legany,2006)andDavies-BouldinIndex(Davies,1979))toprovethecontribution

oftheGAinthesamplingphaseofthesegmentationprocess.

2. THe GeNeTIC ALGORITHMS 2.1. Introduction

Genetic algorithms are designed using Darwin’s natural selection mechanism and Mendel’s

combinationmethodstosolveproblemsanddealwithdifficultsituationssuchasunforeseensituations,

unknownenvironments,changingconstraintsinducedbytheenvironment.

Thismethodworksonapopulationofmanydifferentpotentialsolutions.Iteliminatestheweakest

elementstopromotetheconservationandreproductionofthemost“performing”individuals(the

fittestones,thebestadaptedones).

Therecombination(reproductionbygenetichybridization)ofthestrongestindividualsallows

togivebirthtoevenbetterindividualsfromonegenerationtothenext.

Ourstrategyisbasedontakingadvantageofthesecharacteristicswhichmakethestrengthof

geneticalgorithmstopreparegoodsamplestotheclassificationprocess.(Holland,1975) 2.2. Design of Genetic Algorithms

SimplicityandefficiencyaretwoofthemostattractivefeaturesofGAapproach.Theimplementation

ofageneticalgorithmneeds(Harrat,2003),(Goldberg,1994):

1. Ageneticrepresentationoftheproblem,whichmeansanappropriatecodingofthesolutionsinto

chromosomes.Thisstepassociatetoeachpointofthesearchspaceadatastructure.Itusually

takesplaceafteraphaseofmathematicalmodelingoftheproblem.Thequalityofthedatacoding

conditionsthesuccessofthegeneticalgorithms;

2. Amechanismtogeneratetheinitialpopulation.Thismechanismmustbeabletoproduceanon- homogeneouspopulationthatwillserveasabasisforfuturegenerations.Choosingtheinitial

populationisimportantbecauseitinfluencesthespeedoftheconvergencetotheglobaloptimum;

Inthecasewherenothingisknownabouttheproblemtobesolved,itisessentialthattheinitial

populationshouldbedistributedovertheentireresearcharea.

3. Adedicatedfitnessfunctiontomeasuretheforceofeachchromosome;

4. Amodeofselectionofthechromosomestobereproduced;

5. Operatorstodiversifythepopulationovergenerationsandtoexploretheresearchspace;the

crossingoperatorrecomposesthegenesoftheexistingindividualsinthepopulation;themutation

operatoraimstoguaranteetheexplorationoftheresearchspace.

6. Valuesoftheparametersusedbythealgorithmsuchas:Sizeofpopulation,totalnumberof

generationsorstoppingcriterion,crossingandmutationprobabilities;

2.3. Genetic Algorithms Operating Steps

Ageneticalgorithmtypicallyoperatesthroughasimplecycleoffoursteps(Harrat,2003)

1. Creationofapopulationofchromosomes

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2. Assessmentofeachchromosome 3. Selectionofthebestchromosomes

4. Geneticmanipulation,tocreateanewpopulationofchromosomes

ThecycledescribedinFigure1isinspiredbygeneticterminology.Duringeachcycle,anew

generationofsolutionsoftheproblemisobtained.Initially,aninitialpopulationisgeneratedwhere

eachindividual-solutionofthepopulationiscodedasastringofcharacters(chromosomes).

Then,anassessmentofeachchromosomewillbeestablished.Thisassessmentisdoneby

measuringthequalityofthechromosomesusingtheassessmentfunction(fitness).Thisitallowsto

selectthemostsuitablechromosomesandthusapplythegeneticoperators(crossingandmutation),

whichcreatesanewgeneration.

Attheendofthecycle,anewpopulationisacquired,openingthewayforanewgenerationand

consequentlyanewcycle.

2.4. Algorithm Details

Preparationofsamplesisaverydelicateoperation,itmustbecarriedoutbyanexpertinremote

sensingdomain,becausethequalityofthesesamplesinfluenceswithoutdoubtthequalityofthe

segmentationprocess.Badspecimenscertainlyleadtoerroneousresultsdespitetheefficiencyand

potencyoftheclassificationmethod.

Ouraimistotakefulladvantageofgeneticalgorithmstopreparethebestandmostoptimal

samplesbyminimizingthedistancebetweenpixelsofthesamepopulation(candidateclass)and

maximizingthedistancebetweenthecentroidsofthesepopulations.

Thesepopulationsthusgeneratedmustalwayskeepthesamecentroidresultingfromthemanual

selectionofthesamples,itshouldbenotedthatthepixelsgeneratedduringthisphasearemostly

fictitious.

Thealgorithmwehaveadoptedusesthefollowingrealcoding:

Theindividualiscomposedof30genes,eachgenehasaredorgreenorbluepixelvalue,15genes

forurbansamples(5pixels)and15genesfornon-urbansamples.Eachpixelhasavaluebetween0

and255.Figure2showscodingindividuals.

2.5. The Stages of The Proposed GA Process

1. Initialization:Generaterandomlyapopulationofnindividuals(pixels);

2. Evaluation:Calculatethefitnessfunctionf(x)foreachindividualxofthepopulation;

3. Selection:Inourcaseweusethetournamentselection:wechoosetwoormoreindividualsand

selectthestrongest.ThisprocessisrepeatedseveraltimesuntilobtainingNindividuals(N=50).

Theadvantageofsuchselectionistopreventaverystrongindividualfrombeingselectedseveral

times(Harrat,2003).

Figure 1. Genetic cycle

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4. Crossover:Applytherealcycliccrossoversearchoperatorontheparentswiththeprobabilityof

crossoverPcassociatedtogivechildren.Ifthereisnocrossing,thechildrenarethesamecopy

oftheparents(Hang,2016)

5. Mutation:Applythemutationoperator Pmonchildrenwiththeassociatedmutationprobability 6. Accept:Placethechildreninthenewpopulation

7. Replace:Usethegeneratedpopulationtorunthealgorithm

8. Test:Ifthestopconditionissatisfied,stop,andreturnthebestsolution.Otherwisegotostep2 2.6. Parameters Values

Thevaluesofsuchparametersdependstronglyontheproblemtobestudied.Thus,thereareno

parametersthataresuitableforsolvingalltheproblemsthatcanbeposedtoageneticalgorithm.

However,somevaluesareoftenused(definedintheliterature)andcanbegoodstartingpointsfor

startingasolutionsearchusingaGA.

Basedontheobservationthattheparametervaluesofthevariousoperatorsarethemselves

unknownandindefinitelyimprovedonlythroughexperiments.Someauthors,suchas(Novkovicet

al.,1997)haveproposedtouseakindofMeta-GA:onetofindtheoptimalindividualandtheother

tofindtheoptimalvalueoftheparameters.Thesetwoalgorithmswouldthenrotatesimultaneously

orsequentially.However,itisinevitablethatthecalculationtimewillincreaseaccordingly.(Thomas,

2004)

In2003,thesameauthors(Nokovic,2003)proposedanovelalgorithmcalledGeneticAlgorithm

withself-generatedRandomparameters(GAR),ratherthantheprogrammerpredeterminethe

parametervalues,chromosomeportionswhichdonottranslateintofitness“geneticresidual”are

givenfunctiontodiversifycontrolparametersfortheGA,providingrandomparametersettingalong

theway,anddoingawaywithfine-tuningofprobabilitiesofcrossoverandmutation.(Nokovic,2003) 1. The Probability of Crossover:ChoosingthesuitablevaluefortheprobabilityofcrossoverPc

aswellastheprobabilityofmutationPmdependsonthefitnessfunction.Itschoiceisgenerally

heuristic,morepcishigher,andmorethepopulationundergoesmajorchanges.Ingeneral,

acceptedvaluesarebetween0.5and0.9(Thomas,2001)

2. Themutationprobabilityisselectedintheinterval(0.001,0.01).Pmmustbelowsinceahigh

ratemayleadtoasuboptimalsolution.

3. The Fitness Function:ItisdefinedastheEuclideandistancebetweenthetwocentroidsorurban

andnon-urbanregionscentroid1 (R1, G1, B1)andcentroid2 (R2, G2, B2)representingurban

andnon-urbanpopulation.

F = ( R 2 − R 1 )

2

+ ( G 2 − G 1 )

2

+ ( B 2 − B 1 )

2



Inthisapproachweempiricallyfixthecrossoverprobabilityto0.5andthemutationrateat0.0015.

Figure 2. Coding individuals

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3. THe KOHONeN MAPS

TheKohonenmapalsocalled“SelfOrganizingMaps:SOM”areanautomaticclassification

technique(clustering,unsupervisedlearning),theyweredefinedbyTuevoKohoneninthelate

1980’s(Kohonen,1990).Theyaimtoproduceagroupingsothattheindividualsinthesamebox

aresimilar,theindividualsindifferentboxesaredifferent.Oncloserinspection,werealizethatthe

learningalgorithmisasophisticatedversionoftheK-Meansmethod.

Theaimofunsupervisedclassificationistoorganizethedataintohomogeneousclasseswithout

supervisedlearning,eachclassregroupingindividualshavesimilarfeatures.

Thesenetworksareinspiredbythebiologicalobservationsofthefunctioningofthenervous

systemsofperceptionofmammals.Figure3indicatesthatthenetworkconsistsoftwolayers,the

firstcontainstheinputs,andthesecondconsistsoftheassociatedoutputs.Eachneuronofthelatter

isconnectedtoallinputneuronsthroughnconnectionsthatcarrytheweights.

3.1. Operating Principle

Thismodelusescompetitivelearning,itmeansthatasingleneuronwillbeactivated,afterthe

propagation,thelatterwillthenbeencouraged(itmeansthatonlythewinningneuronaccordingto

acertainfunction,aswellasinsomecases,acertainneighborhoodofthisnode,willseeitsweight

modified).Thewinningneuroniscalculatedbychoosingtheonewhoseweightvectorisclosestto

theinputvectoralongEuclideandistance.Learninginvolvesbringingtheweightsofthewinning

neuronsclosertotheinputvalues.

Kohonenmapsareusefulinthecaseofautomaticclassification.Indeed,theneuronsofthe

Kohonenlayerareusedasindicatorsofbelongingtoagivenclass.

Foracertainneuron,theweightvectorWassociatedwithitindicatestherepresentativeofthe

classthatthisneuronhasthetaskofsymbolizing.Thisneuronwillbeactivatedonlyiftheelement

presentedtothenetworkisclosertotherepresentativeofthisclassandtherepresentativesofthe

otherclassesaremoreorlessfaraway.

Ofcourse,theproblemofKohonenmapstoperformtheautomaticclassificationconsistsin

fixingthenumberofclassesand,atthesametime,thenumberofneuronsintheKohonenlayer.

Concerningthedynamics,activationoftheKohonennetworkbyapplyingtheinputvectorX,

amountstofindingthewinningneuronj(searchfortheminimumEuclideandistance):

j p ( ) = min

i

{ X W p

i

( ) } ; i + 1 2 , , , n 

Theirunsthroughtheoutputlayerandnrepresentsthenumberofneuronsinthedecisionlayer

(output).

Thelearningiscarriedoutinthefollowingway(Jadouin,1994)

Figure 3. Kohonen maps

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Wik p ( + 1 ) = a  Xk Wik p i ( )  ∈ Aj p ( )

0

* ,

, iAj p ( )

 

  

with:

Aj(P)theneighborhoodofthewinningneuronj, P:thenumberoftheiteration,

K:runsthroughallinputneurons, α:thelearningrate,

Wi:theweightvectorassociatedwiththeoutputneuronI.

3.2. The steps of Designing a Network

Beginnersareoftensurprisedtolearnthattobuildaneuralnetwork,thefirstthingtodoisnotto

choosethetypeofnetworkbuttochooseitssamplesoflearning,testingandvalidationdata.Only

thenwillthechoiceofthetypeofthenetworkbemade.Inordertoclarifytheideas,wepresentin

thefollowingthefourstepsthatshouldguidethecreationofaneuralnetwork.

3.2.1. Choosing Samples

Theprocessofdevelopinganeuralnetworkalwaysbeginswiththeselectionandthepreparationof

datasamples.Asindataanalysis,thisstepiscrucialandwillhelpthedesignertodeterminethemost

appropriatenetworktypetosolveareproblem.Thewayinwhichthesampleispresentedaffects:

thetypeofnetwork,thenumberofinputneurons,thenumberofoutputneurons,andhowlearning,

testingandvalidationwillbeconducted.

3.2.2. Network Topology

Thebasicprinciplesofthenetworkprocess,inspiredbythebiologicalobservationsontheordering

ofneurons,wereintroducedin1981byTeuvoKohonen(Kohonen,1990).

Theneighborhoodconceptisoftenusedtohavearegularmapofchangesandtoavoidnoise

phenomena.Thus,eachentryoftheneuralnetworkisnotanisolatedpixel,butapixelaccompanied

byitsneighborhood,(Alexandre,2008)

Thenetworkwehaveadoptedusealineartopologyneighborhoodradiusequalto1inwhich

eachneuron(pixel)isconnectedto2neighbors.

3.2.3. Machine Learning

Learningconsistsfirstofallofcalculatingtheoptimalweightsofthedifferentlinks,usingdatasamples.

3.2.4. Validation and Testing

Thetestsconcerningtheperformanceverificationoftheneuronnetworkoutofsamplesandits

capacityofgeneralization,thevalidationissometimesusedduringthelearning.Oncethenetwork

hasbeencalculated,itisalwaysnecessarytocheckifournetworkreactscorrectly.Thereareseveral

methodsofvalidation:crossvalidation,bootstrapping...butforthetests,generally,apartofthesample

issimplyremovedfromthelearningsampleandkeptfortestingoutofsample.Forexample,60%

ofthesamplescanbeusedforlearning,20%forvalidationand20%fortesting.Inthecaseofsmall

samples,suchadistinctioncannotalwaysbeusedsimplybecauseitisnotalwayspossibletohave

sufficientdataineachofthegroupsthuscreated.Proceduressuchascross-validationaresometimes

usedtoestablishtheoptimalnetworkstructure.

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3.3. The Stages of Kohonen Map Algorithm

1. Initialization:Synapticweightsshouldberandomlyinitiated;

Giveasmallpositivevaluetothelearningrateα;

Setthenumberofiterations(stopcriterion)

2. Presentation:Randomselectionofanentryx(pixel)inthelearningdatabasetopresentitto

thenetwork;

3. Similarity:Searchforthewinningneuron(x)usingtheminimalEuclideandistancecriterion

(nearestneuronorpixel);

4. Updating:UpdatetheweightsofneuronsbelongingtothetopologicalneighborhoodΛ

(x)

(n)

followingtheadaptationstepη(n);wherenistheiterationnumber;

AdjustthelearningparametersΛ

(x)

(n)andη(n).

5. Continuation:Returntostep2untiltheiterationnumbercriterionisreached.

3.4. The Advantages and Disadvantages of the SOM

Fortheadvantagesofneuralnetworks,wecanmention:(Hadjila,2003)

• Automaticlearningofweights(learningrules);

• Generalizationpower(thetestingphase);

• Possibilityofparallelism(theelementsofeachlayercanoperateinparallel);

• Resistancetofailure(ifaneuronnolongerworks,thenetworkdoesnotdisturb);

• Modelingofunknownfunctions;

• Goodapproximation.

Ontheotherhand,neuralnetworkshavesomedisadvantages:(Hadjila,2003)

• Complexrepresentation(usuallytheneuralnetworkarchitectureiscomplex,andevenifitis

schematized,itremainsunreadable);

• Difficultytointerpretparameters(blackbox);

• Non-existentofrulesdefiningthearchitectureofthenetworkaccordingtotheproblemtobe

solved;

• Thelearningalgorithmstakeaconsiderableamountofcomputingtime.

4. ReMOTe SeNSING DATA SeT

Reducethevolumeofdatabyselectingthemostusefuldata.Thechannels(7forLandsatTM)

ofthesamesceneareoftencorrelatedwitheachother,resultingininformationredundancy.The

transformationisusedtosynthesizetheinformation.

Thecomputingofthesenewsyntheticinformationlayersisbasedonstatisticalanalysismethods

suchasprincipalcomponentanalysis(PCA)andarithmeticcombinationsofchannels(alsocalled

spectralratios)leadingtothecreationofnewchannels.

ThePCAiscarriedoutaccordingtotraditionalstatisticalmethods,theparticularityforremote

sensingliesintheverybigquantityofdatatobeprocessed(thepixels).Thegeneralapproachconsists

indefining,inthemultispectralspace,newchannelssummarizingtheinformationcontainedinthe

image.Thismethodaimstomaximize(statistically)thequantityofinformation(orvariance)ofthe

originaldatainarestrictednumberofcomponents.(Thomas,2001)

InourcaseweuseLandsat5thematicmapperimageswhichhavesevenspectralbandswith30m

spatialresolution.SubsequentstudieshaveshownthattheTM4,TM3andTM1channelsholdthe

overwhelmingmajorityoftheinformationcontainedinthewholeimage(Christopher,2004).Table

1showstheLandsat5TMbands.

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4.1. Preprocessing and Reduction of the Size of the Data Space

Inthisstudy,theLandsatimagesuseddidnotrequireradiometricorgeometriccorrections,insofar

astheywerepreviouslyselectedfortheirqualityandthattheUSGSautomaticallyproceedstothe

improvementsoftheimages.Thus,thestepsofcorrectingthedistortionsinducedbytherotation

oftheEarth,itscurvature,thepitchofthesensor,theorientationandtheaspectratiowereavoided.

OurchoicefocusedonTM4,TM3andTM1bandstohighlighttheurbansubjectonthesatellite

image,wejudgedthatthesethreebandsarethemostusefulandthemostsuitabletoachieveourgoal, Ononesidewewillreducethespacetobestudiedfrom7to3withoutlosingasignificantspectral

informationandontheothersideitwillfacilitatethetaskofvisualizationincoloredcompositionby

affectingTM4toRed,TM3toGreenandTM1toBlue.

5. IMPLeMeNTATION OF THe APPROACH

Ourapproachisbasedontheoptimizationofthesamplesbythegeneticalgorithms,thesesamples

whicharemostlyfictitiousanddescendingfromrealexistingparentswillbeusedtolaunchthe

classificationbytheKohonenmapsinordertohighlightandisolatetheurbanthemeonsatellite

images.Preparingsamplesofgoodqualityandquantityissometimesdifficult;usingGAtechnique

hasgivenagreatbenefit;wearenotobligedtotakealargequantityofpixelsfromeachregionto

learnthekohonenmaps.Takingsomepixels(5fromurbanregionand5fromnon-urbanregion)

wassufficientforusgenerateagoodpopulationofindividualswhicharemostlynotrealbutthey

holdtheircredibilityfromtheGAdesign,thelattertechniqueisinspiredfromthenaturalevolution

ofthespecies.

Oncethesamplesareready,wewillusethemtolearntheKohonenmaps.

5.1. The Diagram

Thediagrambelow(Figure4)showsthedifferentphasesoftheclusteringprocessofthesatellite

imagefromtheinputdata(compositecoloredimage)andthefinalclusteredimage.

Table 1. Landsat5 TM bands (Landsat, 2017)

Spectral band Resolution Utilization

TM1 0,45-0,52µm

visible 30mx30m Differentiationground/plants,

coastalareas

TM2 0,52-0,60µm

visible 30mx30m Vegetation

TM3 0,63-0,69µm

Visible 30mx30m Differentiationofplantspecies

TM4 0,76-0,90µmnearinfrared 30mx30m Biomass

TM5 1,55-1,75µm

near-infrared 30mx30m Differentiationsnow/clouds

TM6 10,4-12,5µm

Thermal 30mx30m Thermal

TM7 2,08-2,35µm

mid-infrared 30mx30m Lithology

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5.2. evaluation Tools 5.2.1. Validity Measure (VM)

VMisoneofthecluestotestthevalidityofclusteringalgorithms.VMiscommonlyusedinthe

applicationofimagesegmentationbasedonclustering(Ariana,2014;Chandok,2012).VMis

calculatedusingthefollowingequation:

VM y intra inter

= 

 



 

 

whereintraistheintraclusterdistance,interistheinterclusterdistance,andyisafunctionofthe

numberofclusters.Thefollowingequationisusedtofindthevalueoftheintra-clusterdistance.

intra

N x z i

i k

x c

= −

= ∈

1 ∑∑

1

2 i

  _   

where Nisthetotalnumberofpixelsintheimage,kistheclusternumber,andZiistheclustercenterCi.

Inadditiontocalculate VM,wetaketheminimumvalueoftheinter-clusterdistance(Ray,1999).

Thefollowingequationisusedtofindtheinter-clusterdistance.

inter = min ( z

i

z

j

) 

wherei = 1,2…k,andj = i+1, …, k

yismultipliedbythequotientbetweentheintra-clusterandinter-clusterdistance.Thefollowing

equationisusedtocalculatey.

y = c N . ( , ) 2 1 + 1 

Figure 4. General scheme of the proposed approach

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wherecisaconstantvalueintherange15to25.N(2.1)isaGaussianfunctionforthenumberof

clusters(k).TheGaussianisrepresentedbythefollowingequation.

N e

k

( , ) µ σ

πσ

µ

=

σ

 −





1

2

2

2 2

2



VMhastobeatminimumtogetanoptimalresultandthereforegetwellseparatedclusters

(Palus,2003).

5.2.2. Davies-Bouldin Index (DBI)

DBIwasintroducedin1979byDavidL.DaviesandDonaldW.DBIisusedasametrictoevaluate

theclusteringalgorithms.(Davies,1979)

DBIisafunctiontomeasuretheratiooftotaldispersionwithintheclusterandseparationbetween

cluster(distancebetweenclusters)(Maulik,2002).Thefollowingequationisusedtocalculatecluster

disparity.

S

i

T x z

i

x Ci

= −

1 

whereTiisthenumberofmembersingroup(Ci),andZiisthecenterofithcluster.

DistancebetweenclustersiscalculatedbytheEuclideandistancebetweenthecenterofith

clusterandjthcluster,thefollowingequationisusedtocalculatethedistance.

d

ij

= z

i

z

j



Rijistheratiovaluebetweenithandjthclusterwhichiscomputedbyequation

R s s

ij

d

i j

ij

= +



Findingthemaximumvalueoftheratio(Di),willbeusedtofindthevalueofDBI.Thefollowing

equationexplainhowtocalculatethevalueofDi.

D

i

= max

j j i:

R

ij



ThentheDBIvaluecanbecalculatedusingthenextequation;

DBI k D

i

i

=

k

=

1

1



whereKisthenumberofclusters.

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Infact,theDBIindicatestheoptimalityscoreofaclustering,loweritis,andmoretheclustering

isoptimalandgiveswellseparatedclusters.

6. ReSULTS AND DISCUSSIONS

IntheimagesegmentationN°1(Figure5),theuseofKohonenmapswithouttheGA(image(b)

infigure5)givesasomewhaterroneoussegmentation,anditisfoundthatsomeforestzoneshave

beenclassifiedasbeingurbanarea;andanotherpartofthesearightattheedgeoftheportswasalso

allocatedtourbanareas;thisisduetotheirdirectproximitytourbanareas,forestsandthepollution

oftheseaintheproximityoftheports.

Image(c)(Figure5)showstheresultofthesegmentationofthesameimagebycouplingthe

GAandtheKohonenmaps:thedefectsdetectedintheimage(b)(Figure5)arerepaired,therateof

goodclassificationhasclearlyincreased,anditisprovedbythecalculatedVMandDBIindicesand

showninTable2.ThisprovestheeffectivenessofthegeneticalgorithmscouplingKohonen’smaps

inthedetectionofurbanareasonnoisyandfuzzysatelliteimages.

Figure 5. Segmentation of image N°1 (San Francisco city, USA)

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ConcerningtheimageN°2(Figure6),whichshowstheresultofthesegmentationbythetwo

techniqueswithoutandwiththeGA,wevisuallyobservetheclearfailureofthefirsttechniqueinthe

detectionofvariousurbanareas,falseandlargenon-urbanareasappeartobeurban.

Asfortheapproachwepropose,ithasconsiderablysucceededinminimizingtherateof

misclassificationwithoutgivinggoodresults,thisisduetothenatureofthesatelliteimageitself.

Infact,therehasbeenahugeprobleminacquiringLandsatsatelliteimageryofverygoodquality

andupdatedontheregionofSAIDA(ALGERIA).ThemeasurementsinTable2arguethiscomment.

Finally,theimagesegmentationN°3(Figure7)consolidatesthechoicewemade,thecouple

GA-Kohonenmapshavealwayssucceededinminimizingtheclassificationerrors,asimplevisual

analysiscanconfirmitandmeasurementsareperformedanddisplayedinTable2.

Figure 6. Segmentation of image N° 2

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7. CONCLUSION

Inthispaperweproposeacombinationoftwotechniquestohighlighttheurbanzoneonremotesensing

images;thefirstoneisthegeneticalgorithms(GA)inordertoprepareanoptimalsamplepopulation

generatedfromrealsamplepixelspickedthebothurbanandnon-urbanareas.Thispopulationmostly

fictitiouswillbeintroducedastrainingsamplesforthesecondKohonenmapstechnique.

Ourworkconsistsinimprovingthequalityofthesampleswhichstronglydeterminethequality

oftheclassificationbyusingthegeneticalgorithmssincetheselectionofthefirstrealsamplesare

donemanually.Inthisphase,weincreasethesetsofsamplepixelsbycreatingnewfictitiouspixels

withoutmodifyingtheinitialcentroidofthesamplesmanuallyprepared.Thisoperationaimsto

Figure 7. Segmentation of image N°3

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prepareasolidandhomogeneousgroundonwhichwehavepropelaclassificatoryprocessusing

Kohonenmapsknownbytheirself-adaptingforce.

Weprovetheeffectivenessoftheapproachbyapplyingittosatelliteimagesoftwocities(San

Fransisco,USAandSaida,Algeria).Theobtainedresultsshowthecontributionoftheintroduction

oftheGAintothesegmentationprocess.Toargueourcomments,weusedValidityMeasureand

Davies-BouldinIndex;theanalysisoftheseindicesshowstheeffectivenessoftheproposedapproach.

Infact,eachtimeweusetheGAtopreparethesamples,weobtainthebestresults.

Highlightingurbanareasonmultispectralimagesusingtheproposedapproachaimstohelp

decisionmakersandspecialistsinterritorialplanningtoplanandassestheextensionoftheurban

areas;weusesatelliteimagesasdatasourcesbecausetheyofferaglobalvisiononthegroundin

realandveryshorttime.

Table 2. Segmentation assessment using VM and DBI indices

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