Automatic Change Detection on Satellite Images using Principal Component Analysis, ISODATA and Fuzzy

Volume11,No.6,November-December2022

InternationalJournalofAdvancedTrendsinComputerScienceandEngineering

AvailableOnlineathttp://www.warse.org/IJATCSE/static/pdf/file/ijatcse041162022.pdf https://doi.org/10.30534/ijatcse/2022/041162022

AutomaticChangeDetectiononSatelliteImagesusing

PrincipalComponentAnalysis,ISODATAandFuzzy

C-MeansMethods

BEKKOUCHEIbtissem1,FIZAZIHadria2

1 DepartmentofComputerSciences,UniversityofScienceandTechnologyofOran,Algeria, ibtissem.bekkouche@univ-usto.dz

2 DepartmentofComputerSciences,UniversityofScienceandTechnologyofOran,Algeria, hadria.fizazi@univ-usto.dz

ReceivedDate:October17,2022AcceptedDate:November23,2022 PublishedDate:December06,2022

ABSTRACT

Changedetectionistheprocessofcomparingtwoormore imagesandidentifyingthepartswhereachangehasoccurred. Differencedetectionprocessingbetweensimpledigital images,suchasphotographicimages,iseasytoimplement. Whereasforsatelliteimages,whichcomposeofseveral images’grayscaleandbands,thisrequiresamethodological approachtoimageprocessingappropriatetotheexploitation ofthesedatabecausethiswillallowtofollowtheevolution overtimeofaregionofinterestthroughchangedetection techniques,sotheseimagesareatoolofchoiceinthe managementofnaturalresources.So,inthispaper,we proposeahybridautomaticchangedetectionapproachfor multi-temporalsatelliteimages.Itisbasedonseveral algorithms:ISODATAforautomaticthresholding,Principal ComponentAnalysisastransformationtechnique,Fuzzy C-Meansasclassificationtechnique.Experimentswere performedandassessedbytheiroverallaccuracyandresults validatedtheeffectivenessandefficiencyoftheproposed approach,namedISOFAP.

Keywords: Changedetection,Fuzzyc-meansclustering, ISODATA,Principalcomponentanalysis.

1. INTRODUCTION

Changedetectionisadigitalprocessthatcanbeperformedby traditionalmethodsandusingremotesensingtechnologies. ThebasicthisprocessistomeasurethechangeontheEarth's surfacebyjointlyanalyzingtwoormoretwotemporally separatedimages,inordertolocateandquantify (automatically)thechangesexistingbetweentheseimages [1].Thisisaveryactivesubjectduetopreoccupationabout theconsequencesofglobalandlocalchangesintheearth.

Therearemanychangedetectiontechniquesintheliterature: AlgebraicmethodssuchasImagedifferencingandChange vectoranalysis,…;TransformationmethodssuchasTasseled CapTransformation,PrincipalComponentAnalysis…; ClassificationmethodsasArtificialNeuralNetworks, Comparisonafterclassification…;GeographicInformation

System(GIS)asIntegratedMethodofGISandRemote Sensing,GISApproach…;VisualanalysisusingVisual interpretation;HybridapproachinCombinationofmethods; AdvancedmodelslikeSpectralMixingModel,Li-Strahler ReflectanceModel…[2][3]

Inrecentyears,thistechniquehasbecomeoneofthemost interestingsubjectsintheextractionofinformationfrom satelliteimagesandseveralresearchershaveopted,Theselast years,forthehybridizationofmethodssuchas:in2020, NeelamRuhiletal,havesuggestedanunsupervisedchange detectionmethodbasedonwaveletfusionandtheKohonen Hybrid FCM-σ [4], in the same year, Mohan Singh et al, have proposedanimagefusionusingimagefusionusingimage normalizationandradiometriccalibrationandParticleSwamp OptimizationFuzzyC-Means(PSOFCM).Inthisarticle,an unsupervisedchangeobservationtechniquebasedonthe PSOFCM[5],andin2022,AbdelkrimMaariretal,have proposedanunsupervisedmethodofdetectingchangein satelliteimagesbyfollowingtwomainsteps:Thefirststep focusesondatareductionusingtheIndependentComponent Analysis(ICA)algorithmtoimprovetheefficiencyofthe classifier.ThesecondstageforprocessingusestheFuzzy C-Meansclassificationmethodtofindspecifiedclusters[3].

So,forourpaper,sincethereareseveralchangedetection techniques,themostinterestingattitudewouldbetotryto combinethesetechniquesanddevelopahybridmethod,sofor thatweused:ISODATAforautomaticthresholding,Principal ComponentAnalysis(PCA)astransformationtechnique, FuzzyC-Means(FCM)asclassificationtechnique.

Afterhavingtestedseveralthresholdingalgorithmssuchas: Binarythresholdingonthemean,OTSUthresholdingandEM algorithm[6],wechoseISODATA.Thisisthealgorithmthat isusedforautomaticthresholding,wehavechosenitbecause itiseasytoimplementmorethanitgivesgoodresults.

PrincipalComponentAnalysis(PCA)consistsof transformingvariables,interconnected,intonewvariables unsquaredfromeachotherfordimensionreduction.So,we chosebecauseithasbeenwidelyusedforchangedetection

[7].Ithastheabilitytoprojectthemulti-dimensionaloriginal anditislesssusceptibletoimageoverlapping.

Wefoundthatthemostusedmethodsforchangedetection usedalottheK-means,sowetesteditonourdatabutwe chosetousetheimprovedFuzzyC-Means(FCM)version. FCMhasreducedcomplexityandgivesbestresultfor overlappingdatasetsandcomparativelybetterthank-means algorithm.

2.METHODSUSED

2.1.Isodata

Itisanimagesegmentationtechniquebyclusteringanditis animprovedversionofthek-meansalgorithm,whichwas firstintroducedbyVelascoin1980[8]asaclassicalgorithm whichmakesitpossibletocarryoutaverygood categorizationandwhichgivessignificantresults.[9]

Thethresholdingofanimagecanbedonebymanual thresholdingorbyautomaticthresholding.

Manualimagethresholdinginvolves4steps[9]:

1. Observingthehistogramoftheimage

2. Choiceofthresholdsinthevalleys

3. Definitionoftheclassesoftheregionsbycolorrange

4. Pixelclassification

ISODATAthresholdingisglobalthresholding,whereasingle thresholdisusedacrosstheentireimagetodivideitintotwo clusters.Itallowstofindthevalueofthesoughtthresholdin anautomaticwayfollowingitssteps[10]:

1. ChooseaninitialthresholdT,forexampleT=the averageintensity.

2. DividetheimageintotwogroupsG1andG2usingT.

3. CalculatetheaveragevaluesofeachregionR1andR2

4. Calculatethevalue =( 1+ 2)/2

5. Repeatsteps2to4untiltheTvaluedoesnotchange.

2.2.PrincipalComponentAnalysis

Principalcomponentanalysis(PCA)isamathematical techniqueusedfordataredundancyreductionbyJacksonand Bund,1983[12].Itisusefulwhenyouhaveobtaineddataona numberofvariables(perhapsalargenumberofvariables), andthereissomeredundancyinthesevariables.[12]

PCAisoneofthemostpopularmultivariateanalysis algorithmsforchangedetectionstudiesandcanbeperformed onoriginalornormalizeddata[7].Withthistechniquethe digitalimagesacquiredbyremotesensing,wecanreduceits dimensionalitysuchthatthemultispectralbandsarethe variablestobeintroduced.

TherearecertainstepstofollowtoimplementPCA[7]:

 Takeanoriginaldatasetandcalculatethemeanofthe dataset.  Subtractthemeanforeachdimension.

Calculatethecovariancematrix. 

Calculatetheeigenvectorandtheeigenvalueofthe covariancematrix.

Extractthediagonalofthematrixasavector.

Variancesortingindescendingorder.

Choosecomponentsandformafeaturevector.

Derivationofthenewdataset.

Attheend,thenumberofPCislessthanthenumberof variancesintheoriginalimage.InCDstudies,the consequenceofthislinearizationisthattheunchangedpixels orcommoninformationsharedbyapairofimagesare assumedtobeinanarrowandelongatedspace.Clusteralong aprincipalaxisequivalenttothefirstcomponent(PC1).On thecontrary,pixelscontainingachangewouldbemore uniqueintheirspectralappearanceandshouldliefarfromthis axis(PC2).[13][14]

2.3.FuzzyC-Means

FuzzyC-Means(FCM),isanunsupervisedfuzzy classificationalgorithm.IssuedfromtheC-meansalgorithm, developedbyDunnin1973[15]andimprovedbyBezdekin 1981[16],itintroducedthenotionoffuzzysetinthe definitionofclasses:eachpointinthedatasetbelongstoeach clusterwithacertaindegree,andallclustersarecharacterized bytheircenterofgravity[17].

ThegoalofFuzzyC-Meansclusteringistofindtheminimum ofthefollowingfunction: (1)

where m isanyrealnumbergreaterthan1, uij isthedegreeof membershipof xi inthecluster j.Asarule,foreachpixel,sum ofallmembershipvaluebelongingtoallclassesmustbe1. [3][14]

3.METHODOLOGY

TheFigure1showsandsummarizestheorganizationofthe processingstepsadoptedforthedetectionofchangeson satelliteimagesbyourISOFAPapproachandthisaccording tothebasicprocedureofanimagechangedetection processingsystem[18].

Thisschemeiscomposedofseveralimplementationphases:

1ststep: Thisisadatapreparationstepandwemust:

Dataacquisitionandpreparation: Thisisthestageof collectingandassemblingdata,whichcanbe satelliteimagesandfieldinvestigations.Thenwe cangotopre-processing,forexampleimagecutting ifnecessary. 

Geometriccorrection: theverificationofthe geometricaccuracyisessentialforthedetectionof changesbesidesabadgeoreferencedofmorethan onepixelwouldcauseabnormalresultsforanalyzes pixelbypixel.



Datanormalization: Datamustbenormalized, especiallyforsatelliteimages,inordertoreducethe variabilitybetweenmulti-dateimagesoverthesame geographicalarea.

2ndstep: Thisisadigitalprocessingstepforthedetectionof changesandforthiswewill: 



Applythedifferenceimagemethod

Afterthat,theobtainedimageispartitionedintoH*H blocks,thenthecreationofeigenspacespaceusing PCAandprincipalcomponentsareachieved.

 ApplytheISODATAalgorithmforthresholdingonX.  AfterapplyingPCAandISODATA,createthefeature vectorspaceusingtheeigenspacespace.  Todeterminetheareasthathavechangedandnot changed,weusedtheFCMalgorithmtogeneratethe twoclasses(k=2)andassigneachfeaturevectorto theclassclosesttoeithermodifiedpixelsor unchangedpixels.

3thstep: Thisisageneralizationofthefinalproduct,whichis amapoflandcoverchangesatascaleequivalenttothatofthe inputdata.

4thstep: Onceamodelhasbeendeterminedand implemented,thelaststepistoanalyzeandinterpretthe resultstoestablishthequalityofthismodel.Therearevarious evaluationmeasuresthatcanbeusedandchosencarefully, sincethechoiceofmeasurecaninfluencehowperformanceis assessedandinterpreted.Forthiswehavechosentouse:



Visualinterpretation: theuseofthehumanvisual systemasaqualityjudgmenttoolisnottobe neglectedbutnecessarytoverifythequalityofthe imagesobtainedbytheclassification.Toevaluate thisapproach,wealsousedvisualanalysisaccording tothegroundtruthavailableinthearea.



ConfusionMatrix: Oneofthemostpopularwaysto measuretheperformanceofaclassificationmodel. Eachlinecorrespondstoanactualclassandeach columncorrespondstoanestimatedclassandit includesthefollowingvalues[19]:

o TruePositive,TP,whentheactualclassandthe estimatedclassarebothpositive

o TrueNegative,TN,whentheactualclassandthe estimatedclassarebothnegative

o FalsePositive,FP,whentheactualclassis negativebuttheestimatedclassispositive.This iscalledaType1error.

o FalseNegative,FN,whentherealclassis positivebuttheestimatedclassisnegative.This iscalledaType2error.

Itcanbeusedformorein-depthmeasurementstogeta betterassessmentofthequalityofthemodel.Amongthe classificationmeasuresusedareaccuracy,precision, errorandspecificity.[19]

Accuracyisthenumberofcorrectpredictionsmadeby themodel.

(2)

ThismeasureisusedwhenthenumberofTruePositives andTrueNegativesarethemostimportant. Error=1– Accuracy (3) Precisionisthenumberofcorrectelementsrenderedby themodel.

(4)

ThismetricisusedwhenthenumberofFalsePositives ishighest.

Specificityisthenumberofnegativeclassespredicted bythemodel.

(5)

Figure1: GeneralschemeoftheproposedISOFAPmethod.

4.IMAGESUSED

Imagesdatabase: AsetofRGBimagesof650x650foundin adatabaseusedforthedetectionofchangesinsatellite imageryusingdeep-learning,producedbyHéloïse BAUDHUINandAntoineLAMBOT.[20]

Weappliedourapproachtotwoexamples,presentedin Figure2andFigure3,ofthisdatabase: (a) (b)

(b)

Figure3:Images2(a)beforechangeand(b)afterchange.

ImagesofBoumerdes: Figure4showsahigh-resolution satelliteimageofaLandsat5TMearthquakeandboth acquiredin2003,providedbytheCenterNationaldes TechniquesSpatialesd'ArzewandacquiredbyQuickBird.

Thecharacteristicsofthetwoimagesare:naturalcomposition imagewiththreechannels:TM1,TM2andTM3bandsand theirsizeinpixelsis1002x1002.Theycontaindifferent classeswhichare:asphalt,soil,vegetationandshade,andarea ofdamagetothepost-disasterimage.

(a)

(b)

Figure4:Images3(a)beforedisasterand(b)afterdisaster.

5.EXPERIMENTRESULTSANSDISCUSSION

Thefirstthingforourwork,wedidastudyonaknown changedetectionmethodtounderstanditsconceptandwe chosePCA-FCM.Afterthestudyourproposalwastochange theclassificationalgorithmandreplaceitwithanother,so afterseveraltries,wechosetoworkwiththeFuzzyC-means (FCM)whichisanimprovedversionoftheK-means algorithm.ThenwedidothertestswithPCA-FCMbuteach timewehadtoinitializethethresholdmanuallywiththe variationofthethresholdparametersforeachimage.So,for thatwehaveintegratedanISODATAthresholdingalgorithm tohaveanautomaticthreshold.Intherestofthearticle,some resultsofourproposedmethod"ISOFAP"incomparisonwith PCA-K-means.

Toanalyzeandvalidatetheproposedapproachweusedthree differentdataimagesdescribedinsection4below.

ForthePCA-K-meansparameters,wevariedtheparameters asfollows:numberofclassesis2classes,suchasclass1for changedpixelsandclass2forunchangedones,numberof iterationsbetween70and100,numberofblocks:between2 and5,andThreshold:between10and80.

AndforISOFAPwevariedtheparameterslikethe PCA-K-meansexceptthethresholdbyISODATA.

Figure5showsthefirstperformancetest,wecreatedtwo examplesofsimpleartificialimagestodoourtests,butwe reducedthenumberofiterationsto20andthethresholdto10 becausetheyaresimpleimages.

Bythevisualinterpretation,wenoticethatPCA-K-meanshas someconfusionsandthatthedetectionisnotsocorrect. However,ISOFAPgaveusabetterresultthanPCA-K-means althoughtherearealsoconfusionsbutitisminimal.

Afterconfirmingthecorrectoperationofthetwoalgorithms onasimpleartificialimage,weusedtheimages1ofthe database(presentedinFigure2)andweappliedforthetwo methodsPCA-K-meansandISODATA.Forinitializationof thePCA-kmeansmethod,afterseveraltests,thethresholdwas manuallyinitializedto60,thenumberofiterationsto80and wevariedjustthenumberofblocks(h*h)betweenhequalto 2and5.Wenoticethathedetectedthechangesforhequal2.

ThesameimageappliedtoourmethodISOFAP,thenumber ofiterationsat75,thevariationalsointhenumberofblocks between2and5,andknowingthatISODATAhasinitialized thethresholdto43.fromtheresultswealsonoticethatath equalto2theresultisbetterthantheothers. (a) (b)

Figure5:(a)artificialimage,(b)artificialimagechanged,change mapresults(c)withPCA-K-meansand(d)withISOFAP.

Figure6:Visualinterpretationofthebestresultsby(a)ISOFAPand (b)PCA-K-meansonimages1.

ThepreviousFigures6presentthebestresultsgivenby ISOFAPandPCA-K-meansappliedtoimages1.Fromour visualinterpretationforcomparingthebestresultsofthetwo methodstotheoriginalchangeimage,wenoticethatevenif ISOFAPhadconfusionsanddetectionerrorscausedbythe conflictbetweenthetwochangedandunchangedclasses,it therearesomedetailswerebetterdetectedcomparedto PCA-K-means.

Forimages2,wedidthesametestsasimages1.For PCA-Kmeans,afterseveraltestsweinitializedthethreshold at75,thenumberofiterationsat85andvariedthenumberof blocks.wenotethattheseresultsreinforcethechange detectionresultsonimages1becausethebestresultisgiven onthenumberofblocksequalto2.

ForISODATAthebestresultforimages2isthenumberof blocksat2becauseitgivesmoredetaileddetectionandfewer conflicts,suchasforthisimagethethresholdat20andthe numberofiterationsat93.

Thecomparisonbetweenthebestresultgivenbythetwo methodsapprovesthepreviousresultbecausewecansaythat thevisualinterpretation,presentedinFigure7,isthesameand thatISOFAPhasbetterdetectedthechangeevenifthereare conflictsanddetectionerrorsbuttheyarelessthan PCA-K-means.

Forthelasttests,wechosethebestparameters:numberof blocks(h*h)hat2,numberofiterationsat90andweapplied themtothesamepartoftheimages3(InFigure8).Wenotice thatthePCA-K-meansonlygaveustwoclasses:changedand unchanged,butISOFAPgaveathirdclassofpixelswiththe graycolor,fortheprogramitisconflictsandatthesametime wenoticethatitisnottotallyunchangedbutthechangeisnot great. (a)(b) (c)(d)

Figure8:(a)imagebefore,(b)artificialafter,changemapresults(c) withISOFAPand(d)withPCA-K-means.

FromthevisualinterpretationoftheresultsofourISOFAP approachonthethreegroupsofimages,wenoticethatthere aredetectionerrorsintheimages,whichcanbecausedby severalreasonsamongthemtheresolutionoftheimage becausesometimeswhentheimageisoflowerqualityposes conflictsbetweenthepixelsthereforegiveserrors.

Figure7:Visualinterpretationofthebestresultsby(a)ISOFAPand (b)PCA-K-meansonimages2.

Table1: Evaluationmetricresultsforimages1byISOFAPandPCA-K-means

Method Accuracy Error Precision Specificity Executiontime

ISOFAP 0.8556 0.1444 0.8918 0.1082 65.283269s

PCA-K-means 0.7624 0.2376 0.6881 0.3119 57.882101s

Table2: Evaluationmetricresultsforimages2byISOFAPandPCA-K-means

Method Accuracy Error Precision Specificity Executiontime

ISOFAP 0.6256 0.3744 0.7488 0.2512 75.243269s

PCA-K-means 0.5824 0.4176 0.6181 0.3819 71.874101s

Table3: Evaluationmetricresultsforimages3byISOFAPandPCA-K-means

Method Accuracy Error Precision Specificity Executiontime

ISOFAP 0.7356 0.2644 0.7918 0.2082 55.267169s

PCA-K-means 0.6424 0.3576 0.5841 0.4159 47.817301s

Wefinishourstudybyapplyingtheconfusionmatrix,tothe originalimagesandtheimagesofthebestresults,forextract theinformationthatinterestsusandherearetheresults:

Afterthecomparisonbytheevaluationmetric(inTable1, Table2andTable3)andthevisualinterpretation,wenotice thatourproposedISOFAPapproachgivesgoodresultsto detectchangesandthevaluesofaccuracyandprecisionare highforthreedifferentdataimages,soweconcludethat ISOFAPisbetterthanPCA-K-meanswiththeexceptionof theexecutiontime,ittakeslongerthanPCA-K-means.

6.CONCLUSION

Inthiswork,wehaveaddressedoneoftheimageprocessing operatorswhichisthedetectionofchangesinsatelliteimages. Wehaveproposedahybridmethodbasedontwotechniques todothistreatment.

Ourapproachisbasedonamethodalreadyusedforthe detectionofchanges(PCA-K-means)andwehavetriedto improveit.WeusedISODATAtomakethethresholding automaticandkeptthePCAbecauseitisthemostusedfor changedetectionandtheleastsensitivetoimageoverlap, whilewechoseFCMbecauseithasreducedcomplexityandit isanimprovedversionofthek-meansalgorithm.

Afterthetestsandtheadditions,wearrivedatthe implementationofourISOFAPmethod,whichiscompared withthePCA-K-means,theresultsallowedustoconclude thatourmethodcandetectthechangeandthatitgivesbetter results.

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