Shoreline ExtractionRS

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BEACHMED-e/OpTIMAL BeachErosionMonitoring

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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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Lipakisetal. Shorelineextractionusingsatelliteimagery

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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.

ReerencesBagliS.andSoilleP.(2003)-Morhological automatic extraction o Pan-European coastline rom Landsat

ETM+ images.InternationalSymposiumonGISandComputerCartographyorCoastalZoneMan-

agement.Genova,Italy.

BastinL.(1997)Comparison o uzzy c-means classication, linear mixture modeling and MLC probabilities

as tools or unmixing coarse pixels.InternationalJournaloRemoteSensing,18:36293648.

BerberogluS.,LloydC.D.,AtkinsonP.M.andCurranP.J.(2000)-The integration o spectral and textural

inormation using neural networks or land cover mapping in the Mediterranean.Computers&Geo-

sciences,26:385396.

BraudD.H.andFengW.(1998)- Semi-automated construction o the Louisiana coastline digital land/wa-

ter boundary using Landsat Thematic Mapper satellite imagery.DepartmentoGeography&Anthro-

pology,LouisianaStateUniversity.LouisianaAppliedOilSpillResearchandDevelopmentProgram,OSRAPDTechnicalReportSeries97-002.

ChalabiA.,Mohd-LokmanH.,Mohd-SuanI.,KaramaliK.,KarthigeyanV.andMasitaM(2006)-Monitor-

ing shoreline change using Ikonos image and aerial photographs: a case study o Kuala Terengganu area,

Malaysia.In:ProceedingsotheISPRSMid-termSymposiumProceeding.Enschede,TheNetherlands.

ChenL.C.andShyuC.C.(1998)-Automated Extraction o Shorelines rom Optical and SAR Image.In:Pro-

ceedingoAsianConerenceonRemoteSensing(http://www.gisdevelopment.net/aars/acrs/1998/

ps3/ps3013a.asp).

ChrysoulakisN.(2003)-Estimation o the all-wave urban surace radiation balance by use o ASTER mul-

tispectral imagery and in situ spatial data.JournaloGeophysicalResearch,108:4582(DOI:10.1029/

2003JD003396).

CongaltonR.G.(1991)-A Review o Assessing the Accuracy o Classications o Remotely Sensed Data.RemoteSensingoEnvironment,37:35-46.


14/16


94

DiK.,WangJ.,MaR.andLiR.(2003)-Automatic shoreline extraction rom high-resolution Ikonos satellite

imagery.InproceedingotheAnnualASPRSConerence.Anchorage,Alaska.

ElkoushyA.A.andTolbaR.A.(2004)-Prediction o shoreline change by using satellite aerial imagery.In:

ProceedingotheXXISPRSCongress.Istanbul,Turkey.

ErtezaI.A.(1998)-An automatic coastline detector or use with SAR images.SANDIAReportSAND98-2102.SandiaNationalLaboratories,Caliornia.

ESRI(2005)-ArcGIS 9. What is ArcGIS 9.1?EnvironmentalSystemsResearchInstitute,Caliornia.

FoodyG.M.(2000)- Estimation o sub-pixel land cover composition in the presence o untrained classes.

Computers&Geosciences,26:469478.

FraserC.S.,HanleyH.B.andYamakawaT.(2002a)- Three-dimensional geopositioning accuracy o Ikonos

imagery.PhotogrammetricRecord,17:465479.

FraserC.S.,BaltsaviasE.andGruenA.(2002b)- Processing o Ikonos imagery or sub-metre 3D positioning

and building extraction.ISPRSJournaloPhotogrammetryandRemoteSensing,56,177194.

FrazierP.S.andPageK.J.(2000)-Water body detection and delineation with Landsat TM data.Photo-

grammetricEngineeringandRemoteSensing,66:147167.

FromardF.,VegaaC.andProisyC.(2004)- Hal a century o dynamic coastal change aecting mangroveshorelines o French Guiana. A case study based on remote sensing data analyses and eld surveys .Ma-

rineGeology,208:265280.

GallegoF.J.(2004)-Remote sensing and land cover area estimation.InternationalJournaloRemote

Sensing,25:3019-3047.

GongP.andHowarthP.J.(1990)-The use o structural inormation or improving land-cover classication

accuracies at the rural urban ringe.PhotogrammetricEngineeringandRemoteSensing,56:67-73.

GrodeckiJ.andDialG.(2003)-Block adjustment o highresolution satellite images described by rational

polynomials.PhotogrammetricEngineeringandRemoteSensing,69:59-68.

HarrisP.M.andVenturaS.J.(1995)-The integration o geographic data with remotely sensed imagery to

improve classication in an urban area.PhotogrammetricEngineeringandRemoteSensing,61:993

998.

HuangC.,WylieB.,HomerC.,YangL.andZylstraG.(2002)-Derivation o a tasseled cap transormation

based on Landsat 7 ETM at-satellite refectance.InternationalJournaloRemoteSensing,23:1741-

1748.

KarantzalosK.andArgialasD.(2007)-Automatic shoreline mapping rom panchromatic satellite images.

In:Proceedingso8thPan-HellenicGeographicConerence.GreekGeographicalSociety(inGreek).

KontoesC.C.,RaptisV.,LautnerM.andOberstadlerR.(2000)-The potential o kernel classication tech-

niques or land use mapping in urban areas using 5 m-spatial resolution IRS-1C imagery.International

JournaloRemoteSensing,21:31453151.

Leica(2005)-ERDAS Field Guide.LeicaGeosystems.Atlanta,Georgia,USA.

LiR.,DiK.andMaR.(2001)-A Comparative Study o Shoreline Mapping Techniques.In:Proceedingo

the4thInternationalSymposiumonComputerMappingandGISorCoastalZoneManagement.

Haliax,NovaScotia,Canada.

LiR.,DiK.andMaR.(2003)-3-D Shoreline Extraction rom Ikonos Satellite Imagery.MarineGeodesy,

26:107115.

LiR.(1997)-Mobile mapping: An emerging technology or spatial data acquisition.Photogrammetric

EngineeringandRemoteSensing,63:1165-1169.

LiuH.andJezekK.C.(2004)-Automated extraction o coastline rom satellite imagery by integrating can-

ny edge detection and locally adaptive thresholding methods.InternationalJournaloRemoteSens-

ing,25:937958.

MaraiM.A.,AlmohammadH.,DeyS.,SusantoB.andKingL.(2007)- Coastal dynamic and shoreline

mapping: multi-sourcesspatial data analysis in Semarang Indonesia.EnvironMonitoringandAssess-

ment,DOI10.1007/s10661-007-9929-2.

MaraiM.A.(2003)-Monitoring o the coastal zone dynamics by means o multi-temporal Landsat TM.In:ProceedingsotheannualscienticmeetingXII,Indonesianremotesensingsociety.Bandung.


15/16


95

MartinL.R.G.,HowarthP.J.andHolderG.(1988)-Multispectral classication o land use at the rural-urban

ringe using SPOT data.CanadianJournaloRemoteSensing,14:7279.

MazianH.I.,AzizI.andAbdullahA.(1989)-Preliminary evaluation o photogrammetric-remote sensing

approach in monitoring shoreline erosion.In:Proccedingsothe10thAsianConerenceonRemote

SensingProceeding.KualaLumpur.MillsJ.P.,BuckleyS.J.,MitchellH.L.,ClarkeP.J.andEdwardsS.J.(2005)-A geomatics data integration

technique or coastal change monitoring.EarthSuraceProcessesandLandorms,30:651664.

MostaaM.M.andSoussaH.K.(2006)- Monitoring o Lake Nasser using remote sensing and GIS tech-

niques.In:ProceedingsotheISPRSMid-termSymposium.Enschede,TheNetherlands.

PCI(2003)-OrthoEngine User Guide.PCIGeomatics.Ontario,Canada.

RiddM.K.andLiuJ.(1998)-A comparison o our algorithms or change detection in an urban environ-

ment.RemoteSensingoEnvironment,63:95100.

RyanT.W.,SementilliP.J.,YuenP.andHuntB.R.(1991)-Extraction o shoreline eatures by neural nets and

image processing.PhotogrammetricEngineeringandRemoteSensing,57:947-955.

ScottJ.W.,MooreL.R.,HarrisW.M.andReedM.D.(2003)-Using the Landsat 7 Enhanced Thematic Mapper

Tasseled Cap Transormation to Extract Shoreline.Open-FileReportOF03-272U.S.GeologicalSurvey.ShawB.andAllenJ.R.(1995)-Analysis o a dynamic shoreline at Sandy Hook, New Jersey, using a geo-

graphic inormation system.In:ProceedingsoASPRS/ACSM.pp.382-391.

SteanovW.L.,RamseycM.S.andChristensenP.R.(2001)- Monitoring urban land cover change: An ex-

pert system approach to land cover classication o semiarid to arid urban centers.RemoteSensingo

Environment,77:173185.

StuckensJ.,CoppinP.R.andBauerM.E.(2000)- Integrating contextual inormation with per-pixel classi-

cation or improved land cover classication.RemoteSensingoEnvironment,71:282296.

ToutinTh.(2004)-Review article: Geometric processing o remote sensing images: models, algorithms and

methods.InternationalJournaloRemoteSensing,25,18931924

ToutinTh.(2003a)-Block bundle adjustment o Ikonos in-track images.InternationalJournaloRemote

Sensing,24:851857.

ToutinTh.(2003b)-Error tracking in Ikonos geometric processing using a 3D parametric modelling .Pho-

togrammetricEngineeringandRemoteSensing,69:4351.

TrebossenH.,DefontainesB.,ClasseauN.,KouameJ.andRudantJ.-P.(2005)-Monitoring coastal evo-

lution and associated littoral hazards o French Guiana shoreline with radar images.ComptesRendus

Geosciences,337:1140-1153.

VogelmannJ.E.,SohlT.andHowardS.M.(1998)-Regional characterization o land cover using multiple

sources o data.PhotogrammetricEngineeringandRemoteSensing,64:4557.

VLS(2007)- Feature Analyst Version 4.1 or Imagine.ReerenceManual.VisualLearningSystemsInc.

Missoula,USA.

WangK.D.J.,MaR.andLiR.(2003)-Automatic shoreline extraction rom high resolution Ikonos satellite

imagery.In:ProceedingsotheASPRSAnnualConerence.Anchorage,Alaska.

WhitheK.andElAsmarH.M.(1999)- Monitoring changing position o coastline using thematic mapper

imagery, an example rom the Nile Delta.Geomorphology,29:93105.

Wu T.D. and Lee M.T. (2007) - Geological Lineament and Shoreline Detection in SAR Images.

Proceedings o IEEE Geosciences and Remote Sensing Symposium (IGARSS 2007). Barcelona,

Spain.

ZakariyaR.,RosnanY.,SaidinS.A.,YahayaM.H.,KasawaniI.andLokmanH.(2006)-Shoreline detection

and changes or Terengganu river mouth rom satellite imagery (Landsat 5 and Landsat 7).Journalo

SustainabilityScienceandManagement,1:47-57.

ZangJ.andFoodyG.M.(1998)-A uzzy classication o sub-urban land cover rom remotely sensed im-

agery.InternationalJournaloRemoteSensing,19:2721-2738.

ZhouG.andLiR.(2000)-Accuracy Evaluation o Ground Points rom IKONOS High-Resolution Satellite

Imagery.PhotogrammetricEngineeringandRemoteSensing,66:1103-1112.


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