shoreline extractionrs
TRANSCRIPT
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Shoreline extraction
using satellite imagery
Michalis Lipakis, Nektarios Chrysoulakis,
Yiannis Kamarianakis
Shorelinechangeisconsideredtobeoneothemostdynamicprocessesinthecoastalarea.It
hasbecomeimportanttomapshorelinechangesasaninputdataorcoastalhazardassessment.
Inrecentyears,satelliteremotesensingdatahasbeenusedinshorelineextractionandmapping.
Theaccuracyoimageorthorectiication,aswellastheaccuracyoimageclassiication,arethemostimportantactorsaectingtheaccuracyotheextractedshoreline.Inthisstudy,theshore-
lineotheareaoGeorgioupoliswasmappedortheyears1998and2005usingaerialimagery
andIkonosdata,respectively.Ikonosdatawereorthorectiiedandaeatureextractiontechnique
wasthenusedtoextracttheshoreline.Thistechniqueemployedmachine-learningalgorithms
whichexploitboththespectralandspatialinormationotheimage.Resultswerevalidatedwith
in situmeasurementsusingDierentialGPS.Theanalysisshowedthattherehavenotbeensevere
changesintheshorelinebetween1998and2005,exceptinsomelocationswherechangewas
substantial.
Introduction
Thecoastalareaisahighlydynamicenvironmentwithmanyphysicalprocesses,suchastidalood-
ing, sealevelrise,land subsidence,anderosion-sedimentation.Thoseprocessesplayanimportant
roleinshorelinechangeanddevelopmentothecoastallandscape.Multi-yearshorelinemappingis
consideredtobeavaluabletaskorcoastalmonitoringandassessment.Theshorelineisdenedasthe
lineocontactbetweenlandandabodyowater.Itiseasytodenebutdiculttocapture,sincethe
waterlevelisalwayschanging.Thereore,aproblemexistsinthemappingcommunitybecausedifer-
entpublicorprivateentitieshavecompiledandpublishedshorelinedelineationsthatarebasedon
diferentshorelinedenitions.Thishascreatedconusionanduncertaintyorthosewhouseshoreline
inormationdailyorthesakeodecisionmaking,resourceplanning,emergencypreparednessetc.In
theUSAorexample,NOAAusesthetide-coordinatedshoreline,whichistheshorelineextractedromaspecictidewaterlevel.TheMLLW(MeanLowerLowWater)andMHW(MeanHighWater)areusedin
thiswaytomapshorelinesthatcanbegeo-reerenced.BoththeMLLWandMHWarecalculatedrom
averagesoveraperiodo18.6lunaryears(Lietal.,2001).Incontrast,theU.S.GeologicalSurvey(USGS)
compilesshorelinedataorthe1:24.000scaletopographicbasemapseriesromdigitalorthophoto
quadranglescreatedromphotographsthatarenottidecoordinated,therebymakingtheshorelinea
snapshotintime(Scottetal.,2003).Itisthereoreobviousthatsinceshorelinehasadynamicnature,
itsdenition,mappingandmonitoringarecomplicatedtasks.
Diferentapproachestoshorelinemappingandchangedetectionhavebeenusedinthepast.Tradi-
tionalshorelinemappinginsmallareasiscarriedoutusingconventionaleldsurveyingmethods.The
methodusedtodaybytheAmericanNationalGeodeticSurveytodelineatetheshorelineisanalytical
stereophotogrammetryusingtide-coordinatedaerialphotographycontrolledbykinematicGPStech-niques(Dietal.,2003).Landvehicle-basedmobilemappingtechnologyhasbeenproposedtotrace
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watermarksalongashorelineusingGPSreceiversandabeachvehicle.LiDARdepthdatahavealso
beenusedtomapshorelines(ShawandAllen,1995;Li,1997).
Automaticextractionoshorelineeaturesromaerialphotoshasbeeninvestigatedusingneuralnet-
worksandimageprocessingtechniques(Ryanetal.,1991).Photogrammetrictechniqueshavebeen
employedtomapthetide-coordinatedshorelineromtheaerialimagesthataretakenwhenthewaterlevelreachesthedesiredlevel.Aerialphotographstakenatthesewaterlevelsaremoreexpensiveto
obtainthansatelliteimagery.
Besidesaerialimagery,space-bornradarandespeciallySyntheticApertureRadar(SAR)haveproven
tobeavaluabletoolorcoastalmonitoring.SARimageryhasalsobeenusedtoextractshorelinesat
variousgeographiclocations(Erteza,1998;ChenandShyu,1998;Trebossenetal.,2005;WuandLee,
2007).SAR isa verypromisingtechnology,especiallyorEuropesincethe EuropeanSpaceAgency
(ESA)isrecognizedasaworldleaderinSARmissions(ERS1,ERS2,Envisat,GMES-Sentinel-1).
Inrecentyears,opticalsatelliteremotesensingdatahavebeenusedinautomaticorsemi-automatic
shorelineextractionandmapping.BraudandFeng(1998)evaluatedthresholdlevelslicingandmulti-
spectralimageclassicationtechniquesordetectionanddelineationotheLouisianashorelinerom
30-meterresolutionLandsatThematicMapper(TM)imagery.TheyoundthatthresholdingTMBand5wasthemostreliablemethodology.FrazierandPage(2000)quantitativelyanalysedtheclassication
accuracyowaterbodydetectionanddelineationromLandsatTMdataintheWaggaregioninAus-
tralia.TheirexperimentsindicatedthatthedensityslicingoTMBand5achievedanoverallaccuracy
o96.9percent,whichisassuccessulasthe6-bandmaximumlikelihoodclassication.Scottetal.
(2003)proposedasemi-automatedmethodorobjectivelyinterpretingandextractingtheland-water
interace,whichhasbeendevisedandusedsuccessullytogeneratemultipleshorelinedataorthe
testStatesoLouisianaandDelaware.ThismethodwasbasedontheapplicationoTasseledCaptrans-
ormationcoecientsderivedbytheEROSDataCenterorETM+dataasdescribedbyHuangetal.
(2002).TheTasseledCaptransormationwaschosenoverothermethodsprimarilybecauseotheob-
jectiveandconsistentmannerinwhichitclassiespixelsandbecauseitsuseallowedthecreationo
otheruseulrasterbyproductles.Inoperation,theTasseledCaptransormationrecombinedspectral
inormationothe6ETM+bandsinto3principalviewcomponentsthroughtheuseocoecientsde-
rivedbysamplingknownlandcoverspectralcharacteristics.Othethreeprincipalviewcomponents
created,i.e.,Brightness,Greenness,andWetness,theWetnesscomponentisexploitedtodiferentiate
landromwater.Zakariyaetal.(2006)triedtodetectshorelinechangesortheTerengganurivermouth
andrelatedcoastalarea.LandsatdatawereusedtogetherwithGIScapabilitytodetermineshoreline,
sandyareaandthechangesthatoccurespeciallyonsedimentmovementrom1996to2002.RGBto
IHSimageryconversionanalysisISODATA(IterativeSel-OrganizingDataAnalysis)classicationwere
employed.LiuandJezek(2004),aswellasKarantzalosandArgialas(2007)automatedtheextractiono
coastlineromsatelliteimagerybycannyedgedetectionusingdigitalnumber(DN)threshold.
Lietal.(2001)comparedshorelinesothesameareathatwereextractedusingdiferenttechniques,
evaluated their diferences and discussed the causes o possible shoreline changes.The diferent
shoreline productshadbeengeneratedusingdiferenttechniques:by digitising rom aerialortho-
photos,intersectinga digitalwatersuracewitha coastalterrainmodelandextractingromstereo
satelliteimages.Inaddition,existingshorelinesdigitisedromUSGSmapsandNOAAT-Sheetswere
includedintheiranalysis.
Withthedevelopmentoremotesensingtechnology,satellitescancapturehighresolutionimagery
withthecapabilityoproducingstereoimagery.Thenewgenerationoveryhighspatialresolution
satelliteimagingsystems,suchasIkonosandQuickbird,opensaneweraoearthobservationand
digitalmapping.Theyprovidenotonlyhigh-resolutionandmulti-spectraldata,butalsothecapability
orstereomapping.Becauseotheirhighresolutionandtheirshortrevisitrate(~3days),Ikonosand
Quickbirdsatelliteimagesareveryvaluableorshorelinemappingandchangedetection,thereore
theirdatahavebeenusedinseveralpaststudies.Wangetal.(2003)investigatedanovelapproachor
automaticextractionoshorelineromIkonosimagesusingameanshitsegmentationalgorithm.Dietal.(2003)investigatedanovelapproachorautomaticextractionoshorelinesromIkonosimagery:
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4mand1mresolutionIkonosimagesalongtheLakeErieshorewereused.Intherststeptheimages
weresegmentedintohomogeneousregionsbymeanshitsegmentation.Then,themajorwaterbody
wasidentiedandaninitialshorelinewasgenerated.Thenalshorelinewasobtainedbylocalrene-
mentwithintheboundariesothecandidateregionsadjacenttotheinitialshoreline.Lietal.(2003)
usedIkonosstereoimageryinshorelineextraction.TheypresentedtheresultsoanexperimentinwhichtheyattemptedtoimproveIkonosRationalFunctions(RF)orabettergroundaccuracyandto
employtheimprovedRFor3-Dshorelineextractionusing1-meterpanchromaticstereoimagesina
LakeEriecoastalarea.Inthismethod,a2DshorelineisextractedbymanualdigitisingononeIkonos
image;thencorrespondingshorelinepointsontheotherimageothestereo-pairareautomatically
extractedbyimagematching.The3Dshorelineiscomputedusingphotogrammetrictriangulation.
Chalabietal.(2006)hadusedpixel-basedsegmentationonIkonosimageusingDNthreshold.Thepar-
titionothelandandseaboundarywasdoneusingpseudo-colourwhichexhibitsastrongcontrast
betweenlandandwatereatures.
Shorelinechangeisconsideredtobeoneothemostdynamicprocessesincoastalarea.Ithasbecome
importanttomaptheshorelinechangeasaninputdataorcoastalhazardassessment.Therearemany
change detectiontechniques currently in useincludingvisual interpretation, spectral-value-basedtechnique(diferencing, image regression, DN value analysis), multi-datacomposites, and change
vectoranalysis.Visualinterpretationomulti-temporalimagesorcoastalmonitoringwaspresented
byMazianetal.(1989)andElkoushyandTolba(2004).BagliandSoille(2003)analysedDNvalueus-
ingslicingoperationorchangemonitoring.Inaddition,WhitheandElAsmar(1999)introducedan
algorithmunctionandDNanalysistodeviatethewaterromtheland.TheDNvalueanalysishas
alsobeenappliedonLandsatimages,e.g.byFrazierandPage(2000)andMarai(2003).Fromardet
al.(2004)identiedcoastalchangesthattookplaceoverthelast50years,andrelatedthemtonatural
processeso turnoverandreplenishmentomangroveorests.Theyusedacombinationo remote
sensingtechniques(aerialphotographsandSPOTsatelliteimages)andeldsurveysintheareao
theSinnamaryEstuary,FrenchGuiana.Millsetal.(2005)introducedtheintegrationothegeomat-
icstechniquestoormaccuraterepresentationsothecoastline.AhighlyaccurateDigitalElevation
Model(DEM),createdusingkinematicsGPS,wasusedascontroltoorientatesuracesderivedrom
therelativeorientationstageophotogrammetryprocessing.MostaaandSoussa(2006)haveapplied
GISandremotesensingtechniquetomonitorthelakeo Nasserincludingtheshorelinedynamics.
ThreesatelliteLandsatimagesorNasserLakewereavailableinatimeseries(1984,1996,and2001).
Topographymaposcale1:50.000,thatissuitabletotheresolutionoLandsatimages,wasusedor
developingDEM.Chalabietal.(2006)assessedmulti-datasourcesormonitoringshorelineinKuala
Terengganu,MalaysiausingIkonosandaerialphotographs.Resultsotimeseriesdatawerecombined
toeachothershowingspatialchangeoshoreline.Maraietal.(2007)illustratedtheshorelinedynam-
icsinacoastalareaoSemarang-Indonesiausingmultisourcespatialdata.Inspiteothetechnique
andapproachtoshorelinemonitoringanddelineation,nosinglemethodhasbeenimplementedthat
isreerommajordisadvantages.
Thereore,shorelinesothesameareamaybeextractedatdiferenttimesusingsatellitedataand
representchangesthatappearedindiferenceperiodsthatareillustratedasdiferencesamongthem.
Therearetwopossibleinterpretationsotheshorelinediferences.Oneisthattheshorelineindeed
changedintherealworld.Theotherpossibilityisthatthediferencesareintroducedbyshoreline
mappingerrors.Theaccuracyotheshorelinederivedrom1meterIkonosimageryshouldbeabout
2m-4m(ZhouandLi2000;Lietal.,2001,GrodeckiandDial,2003),consideringtheactthattheac-
curacyo3Dgroundcontrolpoints(GCPs)reaches2m3m,withGCPsandtheaccuracyoidentiying
andlocatingconjugateshorelinepointsisabout1.5pixels(1m-2m).Anoptimisticestimationothe
shorelineaccuracyderivedromthe4-meterIkonosimagesinthisspeciccaseisabout8.5m(Lietal.,
2001).
Inmostotheaorementionedmethods,theshorelineextractionusingIkonosorthoimageryisbased
onlandcoverclassicationtodiscriminatethepixelscorrespondingtowaterbodiesromthosecor-respondingtoland.Following,theresultingthematicimageisconvertedtovectorcoverage,usually
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apolygonshapele(ESRI,2005)containingthepolygonscorrespondingtoeachclass.Theshorelineis
nallyextractedromthepolygonthatcorrespondstowaterbyemployingautomaticorsemi-auto-
maticGISprocedures.Thus,theaccuracyotheimageorthorectication,aswellastheaccuracyothe
imageclassication,isthemostimportantactorsafectingtheaccuracyotheextractedshoreline.
Theorthorecticationaccuracywasdiscussedabove.Concerningclassicationaccuracy,itdependsonthespatial,spectralandradiometricresolutionotheimage,aswellasontheclassicationmethod.
Numerousstudieshavebeencarriedoutusingsatelliteimagestoextractlandcovertypes(Congalton,
1991;RiddandLiu,1998;Martinetal.,1988;GongandHowarth,1990;Chrysoulakis,2003;Gallego,
2004).Themajorityothepaststudiesrelyonremotesensingdatatoclassiylandcovertypesus-
ingeitherrawDNorcalibratedradiancevalues.However,iveryhighspatialresolutiondatasuchas
Ikonosimagesareused,thelandcoverclassicationocoastalareasmaybeproblematicbecauseo
theheterogeneityandsmallspatialsizeothesuracematerials,whichleadstosignicantsub-pixel
mixing(Foody,2000;Kontoesetal.,2000).Thereore,thespatialcontextshouldbetakenintoaccount
inimageclassicationandobjectorientedalgorithmsshouldbeused.Improvementsintheaccuracy
oclassicationhavebeenachievedusingavarietyosophisticatedapproachesincludingtheuseo
neuralnetworks(Berberogluetal.,2000),uzzylogic(Bastin,1997;ZangandFoody1998;),textureanalysis(Stuckensetal.,2000),machinelearning(VLS,2007)andincorporationoancillaryspatialdata
intheclassicationscheme(HarrisandVentura,1995;Vogelmannetal.,1998,Steanovetal.,2001).
Methodology
TheIkonossatelliteprovidesglobal,accurate,high-resolutionimageryormapping,monitoring,and
development.Thepanchromaticsensorwith82cmresolutionandan11.3kmwideswathatnadirpro-
videshighresolution,intelligence-qualityimagery.Themultispectralsensor,simultaneouslycollect-
ingblue,green,red,andnearinraredbandswith3.28mresolutionatnadir,providesnatural-colour
imageryorvisualinterpretationandcolour-inraredimageryorremotesensingapplications.Com-
biningthemultispectralimagerywiththehighresolutionpanchromaticresultsin1-metercolour
images(pan-sharpenproduct),whichcanbeorthorectiedaterwards.Theorthorecticationisneed-
edtoeliminatethegeometricdistortions,whichwillbeexplainedbelow,sothatimageeatureshave
correctplanimetriccoordinates.Quantitativeestimationssuchasshorelinedetectionareperormed
usingorthorectiedimages.
Apartromthediferenttechniquesthatcanbeappliedorshorelineextractionandmonitoringrom
highresolutionsatelliteimages,theprocessingchainconsistsotheollowingbasicsteps:
acquisitionoimagesandpre-processing;
acquisitionothegroundControlPoints(GCPs)withimagecoordinatesandmapcoordinates;
computationotheunknownparametersothemathematicalunctionsusedorthegeometric
correctionmodel;
imageorthorecticationusinganappropriateDEM;
automatic,semi-automaticormanualshorelineextractionromtheorthorectiedimagery;
monitoringoshorelinechangesbyrepeatingtheabovestepsatpredenedtimeperiodsand
comparingtherelativepositionsotheextractedshorelines.
Thus,beoretheapplicationoanyalgorithmorautomaticextractionoshorelinerommultispectral
satelliteimages,theseimagesshouldbeothorectiedtotakeintoaccountthegeometricdistortions
duringimageacquisition,aswellastheefectotopography.Eachimageacquisitionsystemproduces
uniquegeometricdistortionsinitsrawimagesandconsequentlythegeometryotheseimagesdoes
notcorrespondtotheterrainorocoursetoaspecicmapprojection.Obviously,thegeometricdis-
tortionsvaryconsiderablywithdiferentactorssuchastheplatorm,thesensorandalsothetotal
eldoview.However,as ithasbeendescribedbyToutin(2004),it ispossibletomakegeneralcat-
egorisationsothesedistortions.Thesourcesodistortioncanbegroupedintotwobroadcategories:
theobserverortheacquisitionsystem(platorm,imagingsensorandothermeasuringinstruments,
suchasgyroscope,stellarsensors,etc.)andtheobserved(atmosphereandEarth).Inadditiontothesedistortions,thedeormationsrelatedtothe mapprojectionhavetobetakenintoaccountbecause
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theterrainandmostGISend-userapplicationsaregenerallyrepresentedandperormedrespectively
inatopographicspaceandnotinthegeoidorareerencedellipsoid.Mostothesegeometricdistor-
tionsarepredictableorsystematicandgenerallywellunderstood.Someo thesedistortions,espe-
ciallythoserelatedtotheinstrumentation,aregenerallycorrectedatgroundreceivingstationsor
byimagevendors.Others,orexamplethoserelatedtotheatmosphere,arenottakenintoaccountandcorrectedbecausetheyarespecictoeachacquisitiontimeandlocationandinormationonthe
atmosphereisrarelyavailable.Theremaininggeometricdistortionsrequiremodelsandmathematical
unctionstoperormgeometriccorrectionsoimagery:eitherthrough2D/3Dempiricalmodels(such
as2D/3Dpolynomialor3DRF)orwithrigorous2D/3Dphysicalanddeterministicmodels.With2D/3D
physicalmodels,whichreectthephysicalrealityotheviewinggeometry(platorm,sensor,Earth
andsometimesmapprojection),geometriccorrectioncanbeperormedstep-by-stepwithamath-
ematicalunctionoreachdistortion/deormation,orsimultaneouslywithacombinedmathematical
unction.
2D/3Dphysicalunctionsusedtoperormthegeometriccorrectiondifer,dependingonthesensor,
theplatormanditsimageacquisitiongeometry(Toutin,2004):
instantaneousacquisitionsystems,suchasphotogrammetriccameras,MetricCameraorLargeFor-matCamera;
rotatingoroscillatingscanningmirrors,suchasLandsat-MSS,TMandETM+;
push-broomscanners,suchasSPOT-HRV,IRS-1C/D,IkonosandQuickbird;and
SARsensors,suchasJERS,ERS-1/2,RADARSAT-1/2andEnvisat.
Whateverthegeometricmodelused,evenwiththeRFsomeGCPshavetobeacquiredtocompute/
renetheparametersothemathematicalunctionsinordertoobtainacartographicstandardaccu-
racy.Generally,aniterativeleast-squareadjustmentprocessisappliedwhenmoreGCPsthanthemini-
mumnumberrequiredbythemodel(asaunctionounknownparameters)areused.Thenumbero
GCPsisaunctionodiferentconditions:themethodocollection,sensortypeandresolution,image
spacing,geometricmodel,studysite,physicalenvironment,GCPdenitionandaccuracyandthenal
expectedaccuracy.Theaerialtriangulationmethodhasbeendevelopedandappliedwithdiferent
opticalandradarsatellitedatausing3Dphysicalmodels(Toutin,2003a,b),aswellaswithIkonosdata
using3DRFmodels(Fraseretal.,2002a,b).Allmodelparametersoeachimage/striparedetermined
byacommonleast-squaresadjustmentsothattheindividualmodelsareproperlytiedinandanentire
blockisoptimallyorientedinrelationtotheGCPs.
Asithasbeenalreadymotioned,shorelineextractionneedsorthorectiedimages.Torectiytheorigi-
nalimageintoamapimage,therearetwoprocessingoperations:
ageometricoperationtocomputethecellcoordinatesintheoriginalimageoreachmapimage
cell,eliminatingthegeometricdistortionsaspreviouslyexplained;and
aradiometricoperationtocomputetheintensityvalueorDNothemapimagecellasaunctiono
theintensityvaluesooriginalimagecellsthatsurroundthepreviously-computedpositionothe
mapimagecell.
Thegeometricoperationrequirestheobservationequationsothegeometricmodelwiththeprevi-
ouslycomputedunknowns,andsometimeselevationinormation.The3Dmodelstakeintoaccount
elevationdistortionandDEMsisthusneededtocreatepreciseorthorectiedimages.DEMsimpacton
theorthorecticationprocess,bothintermsoelevationaccuracyorthepositioningaccuracyand
ogridspacingorthelevelodetails.Thislastaspectismoreimportantwithhigh-resolutionimages
becauseapoorgridspacingwhencomparedtotheimagespacingcouldgeneratearteactsorlinear
eaturessuchasshorelines.Foranymapcoordinates(x,y),withthezelevationextractedromaDEM
when3Dmodelsareused,theoriginalimagecoordinates(columnandline)arecomputedromthe
tworesolvedequationsothemodel.However,thecomputedimagecoordinatesothemapimage
coordinateswillnotdirectlyoverlayintheoriginalimage;inotherwords,thecolumnandlinecom-
putedvalueswillrarely,iever,beintegervalues.Sincethecomputedcoordinatevaluesintheoriginal
imagearenotintegers,onemustcomputetheDNtobeassignedtothemapimagecell.Inordertodothis,theradiometricoperationusesaresamplingkernelappliedtooriginalimagecells:eithertheDN
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otheclosestcell(callednearestneighbourresampling)oraspecicinterpolationordeconvolution
algorithmusingtheDNsosurroundingcells(Toutin,2004).
InordertoaccuratelycreateorextractgeographicinormationromrawIkonosimagery,theImage
GeometryModel(IGM)mustaccompanytheimagery.TheIGMconsistsoseveralmetadataleswhich
containRPCs(rationalpolynomialcoecients).TheRPCsareaseriesocoecientsthatdescribetherelationshipbetweentheimageas itexistedwhencapturedandtheEarthssurace.Althoughthey
donotdescribesensorparametersexplicitly,RFsaresimpletoimplementandperormtransorma-
tionsveryrapidly.WiththeavailabilityoRPCs,theIkonosinteriorandexteriororientationsarevery
accurate.ThereoreIkonosimagerycanbeorthorectieditheIGM,anaccurateDEMandsomeGCPs
areavailablebyemployinganyphotogrametricsotwaresuchasOrthoengine(PCI,2003)orLeica
PhotogrammetrySuite(Leica,2005).
Thenextsteporshorelineextractionisthewater-landseparation;thereoretheorthorectiedimage
shouldbeclassiedorapolygoncorrespondingtowater(orland)areashouldbeextracted.Taking
intoaccounttheaorementionedlandcovermappingconstraintsorveryhighspatialresolutionsatel-
litedata,amachinelearningclassierapproachseemsthebestsolutionorIkonosmultispectralimage
classication.Thistypeoclassierusesaninductivelearningalgorithmtogenerateproductionrulesromtrainingdata.Aswithaneuralnetwork,thereareseveraladvantagestousingamachine-learn-
ingapproach.Sinceancillarydatalayersmaybeusedtohelpimprovediscriminationbetweenclasses,
ewereldsamplesaregenerallyrequiredortraining.Thismachinelearningmodelisnon-parametric
anddoesnotrequirenormally-distributeddataorindependenceoattributes.Itcanalsorecognize
nonlinearpatternsintheinputdatathataretoocomplexorconventionalstatisticalanalysesortoo
subtletobenoticedbyananalyst.FeatureAnalystsotware(VLS,2007)wasselectedorshoreline
extractionromIkonosimagery,sinceitemploysmachine-learningtechniqueswhichhavethepoten-
tialtoexploitboththespectralandspatialinormationotheimage.Itprovidesaparadigmshitto
automatedeatureextractionsinceit:(a)utilisesspectral,spatial,temporal,andancillaryinormation
tomodeltheeatureextractionprocess,(b)providestheabilitytoremoveclutter,(c)incorporates
advancedmachinelearningtechniquestoprovideunparalleledlevelsoaccuracy,and(d)providesan
exceedingly simple interace
oreatureextraction.Itworks
by taking a small and sim-
ple set o training examples,
learnsromtheexamples,and
classiestheremainderothe
image. When classiying the
contentsoimagery,thereare
only a ew attributes acces-
sible to human interpreters.
Foranysinglesetoimagery
theseare:Shape,Size,Colour,
Texture,Pattern,Shadow,and
Association. Traditional im-
age processing techniques
incorporate only colour
(spectral signature) and per-
haps texture or pattern into
an involved expert workow
process.Theshorelineextrac-
tionstepsusingFeatureAna-
lystareshowninFigure1.
Figure 1 - Shoreline extraction work
ow (adapted rom VLS, 2007).
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Case Study: Shoreline extraction in the area o Georgioupolis, Crete
TheShorelineextractionortheareaoGeorgioupoliswasperormedortheyears1998and2005
using aerial imageryandIkonosdata,respectively.Theresultsvalidatedwith in situmeasurements
withDiferentialGPS (DGPS)providedbyOANAK.TheHellenicGeodeticReerenceSystemo1987
(EGSA87)wasusedinallcases.
Shoreline extraction using an aerial image acquired in 1998
AnorthorectiedaerialimageandaDEMothebroaderareaoGeorgioupoliswereavailabletoOANAK
aspastprojectproducts.Theaerialimagehasthespatialresolutiono1m,whereasitspositionalac-
curacywasbetterthan2m(RMSExy
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Step 2: Acquisition o GCPs.
AeldcampaignwasorganizedbyOANAKand15GCPswereselectedusingadiferentialGPS.The
locationothesepoints(circles)superimposedtotheroadnetworkothestudyareaisshowninFig-
ure5.Theirpositionalaccuracywasbetterthan1m(RMSExy
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asithasbeenalreadydescribed.TheorthorecticationresultisshowninFigure7asapseudocolour
compositionRGB:3-2-1,withtheroadnetworkothearea(redlines)andtheshorelineextractedrom
theaerialimage(yellowline)superimposed.Thespatialresolutionotheothorectiedimageis1m,
whereasitspositionalaccuracyisbetterthan2m(RMSExy
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Inordertorunamulti-classextraction,thetwodiferentclassesowaterandlandhavetobecom-
binedintoasingle,multi-classlayer.Thereisa built-inunctionoFeatureAnalystorthis.Thepro-
ducedtwo-classlayeristheinputothealgorithm.
Havingtheinput,thenextactionistosetthealgorithmsparameters(VLS,2007).Allourimagebands
areabouttobeincludedandtheimageresolutionissetto1m.Thespectralinormationisderivedromthismultispectralimage,whereasthespatialcontextoreachpixelistakenintoaccountbyad-
justingtheinputrepresentationotheclassicationalgorithm.Theinputrepresentation,thatdeter-
mineshoweachpixelislookedatin relationtoitsneighbours,issettoManhattanrepresentation
(VLS,2007).Manhattanisapre-denedinputpattern,usedmainlyorwatermassandlandcoverea-
tures,likeoceans,lakes,oods,wetland,impermeablesuraces,etc.Thepatternwidthissetto5.This
meansthatconsideringthatthedecisionpixel(redinFigure9)isinthecentreoa55grid,according
totheManhattaninputrepresentation,thealgorithmwilltakeintoaccount13pixels(blueinFigure9),
locatedasshowninFigure9.Computing13pixelsoeachband,thereisatotalo52pixelsthatwillbe
computedtomakeadecisionorasinglepixel.Theminimumaggregateareaissetto500pixels.That
istomaintainrelativeeaturecharacteristics,whiletryingtondalargearea.
Figure 9 - The Manhattan input
representation that is used, withpattern width set to 5.
TheclassicationresultisshowninFigure10.Itisagainamulti-classlayer,whichneedstobesplit.
Althoughtheborderobothresultclassesisthesameline(theshoreline),thewaterclassischosen
ortheextraction.Thebuilt-inunctioninFeatureAnalystthatsplitsoutclasseswasusedtosplitthe
classesandkeeponlythewaterclassasaseparateset.Sinceonlyonepolygonhasbeenletitsborder
wassmoothedinordertoextracttheshoreline.TheBezierSmoothAlgorithm(VLS,2007)wasused,
withtheollowingparameters:thenumberoverticestoeachsidesetto2andthemaximumdistance
eachvertexisallowedtomovesetto3m.TheresultothesmoothedpolygonisshowninFigure11.
Figure 10 - The land water clas-
sication result: a two class result
corresponding to the two-class
input. Both spectral and spatialinormation have been taken
into account.
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Figure 11 - The extracted
smoothed polygon correspond-
ing to water.
Finally,theborderothewaterpolygonwasautomaticallyextractedandconvertedtoalineshapeleollowingastandardGISprocedure(VLS,2007;ESRI,2005).Thislineshapeleisthenalresultothe
shorelineextractionprocedureasitisshowninFigure12,wheretheextractedshorelinehasbeen
superimposedontheorthorectiedIkonosimage.
Figure 12 - Pseudocoloured
composition RGB: 3-2-1 o the
orthorectied Ikonos image with
the extracted shoreline (yellow
line) superimposed.
Step 6: Comparisons - Validation.
The produced shorelines (1998 Shoreline, and 2005 Shoreline) were compared and a Root Mean
SquareError(RMSE)wascomputedtoreecttheshorelinechangeduringthis7yearsperiod.RMSEisaglobalmeasure,thusthemaximumchangewashighlightedanditispresentedbelow.TheRMSE
wasusedasaquantitativeevaluationotheextractedshorelinesaccuracy.RMSEencompassesboth
systematicandrandomerrorsandisdenedas[1]:
[1]
Where:
si=minimumdistanceothetwolinesatapre-denedlocationi(x,y),
n=numberomeasuringlocationsinthisstudy.
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Finally,boththeimage-derivedshorelineswerecomparedwithan in situderivedshorelinewhich
wasproducedby OANAKwiththeuseodiferentialGPS.Thethreelinesareshownin Figure13,
wherea partothestudyareahasbeenextracted(scale1:2.000):The in situderivedshorelineis
presentedinred,the1998Shorelineispresentedingreenandthe2005Shorelineispresentedin
yellow.TheRMSEcalculatedusingthein situlineasbaseline,aswellastheRMSEbetweentheex-tractedshorelinesor1998and2005,areshowninTable1.
Figure 13 - Part o the study area as a pseudocolour composition RGB: 3-2-1 o the orthorectied Ikonos image (scale
1:2000). The in situ derived shoreline is shown in red, the 1998 Shoreline is shown in green, whereas the 2005 Shoreline
is shown in yellow.
Theanalysisshowedthattherewerenotseverechangesinshorelinebetween1998and2005,ex-
ceptinsomelocationswherethechangewassubstantial.Themostimportantchangeisshownin
Figure14,whereapartothestudyareaaroundtheriverwhichisclosetothetownoGeorgioupolis
hasbeenextracted(scale1:2.000):bothlineshavebeensuperimposedontheIkonosorthorectied
image.The1998Shorelineispresentedingreenandthe2005Shorelineispresentedinyellow,asbeore.ItisobviousromFigure14thattheoutallotheriverhasbeenmodiedduringthis7year
period.Ashitoabout50mtotheESEdirectioncanbeobservedinFigure14.
Table 1 - RMSE between a) in situ and aerial image derived shoreline (1998); b) in situ and Ikonos derived shoreline
(2005); c) aerial image and Ikonos derived shoreline.
Pair o Lines RMSE (m)
Insitu1998Shoreline 3.01
Insitu2005Shoreline 5.65
1998Shoreline2005Shoreline 6.46
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Figure 14 - Part o the study area as a pseudocolour composition RGB: 3-2-1 o the orthorectied Ikonos image (scale
1:2000). The 1998 Shoreline is shown in green and the 2005 Shoreline is shown in yellow. A shit o about 50m to the ESE
direction o the river outow is observed.
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