The use of Sentinel 2 data for mapping European landscapes ... · Sentinel 2 imagery can result in...

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The use of Sentinel 2 data for mapping European landscapes: the case of Denmark Henning Sten Hansen, Vlad Rosca, Mark Takacs, Casper Trock, Algirdas Vepstas, Jamal Jokar Arsanjani Geoinformatics research group, Aalborg University Copenhagen

Transcript of The use of Sentinel 2 data for mapping European landscapes ... · Sentinel 2 imagery can result in...

Page 1: The use of Sentinel 2 data for mapping European landscapes ... · Sentinel 2 imagery can result in relatively accurate land cover/land use products. § Improving the accuracy of the

The use of Sentinel 2 data for mapping European landscapes: the case of Denmark

HenningStenHansen,VladRosca,MarkTakacs,CasperTrock,Algirdas Vepstas,JamalJokarArsanjani

Geoinformatics researchgroup,AalborgUniversityCopenhagen

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Background&Introduction

§ Lackofglobalandregionalfinescalelandcoverproducts§ Sentinel2-Aand2-Brecentlylaunched§ Potentialforup-to-date,finescalelanduse/landcoverproducts§ EarlystageofexploringthepotentialofSentinelfordevelopingdiverseapplications

§ FutureEuropeanlandcoverinformationshouldbeimprovedintermsofspatialandtemporalresolution

§ CanSentinelplayabigroleinthefuturegenerationoflandcovermaps

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Objectives

Themainobjectivesofthisstudyare:§ toexplorethepotentialofusingSentineldataforlandcover/usemappingparticularlyinEurope,

§ toprovideacomparisonofseveralpixel-basedimageandmachinelearningalgorithmsfortheclassificationofdiscretelanduseclassesusingremotesensingsatelliteimagery.

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ImageSelection

§ Sentinel2imagery10x10mresolution§ FreeandeasytodownloadattheESAwebsite§ Cloudfree§ NorthernDenmark§ Area12,000km2including120millionpixels§ 9Bands

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LandCoverClassificationNomenclature

§ BasedonIPCC(IntergovernmentalPanelonClimateChange)§ SevenClasses

§ Settlement§ Forest§ Cropland§ Grassland§ Wetland§ OtherLand§ Water(user-defined)

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AlgorithmSelection

§ Supportvectormachines(SVM)§ Randomforests(RF) § MaximumLikelihood(ML)

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Groundtruthdata

§ AnintegrationofdataSources:§ Kort10§ DanishAgrifish Agency§ DanishNaturalEnvironmentPortal

§ Reliable,accurate,andtimelydata

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SelectionofSamplePoints

§ Equalizedstratifiedsampling

§ Equalnumberofrandom,spatiallydistributedpointsselectedfromeachclass

§ Selectedcellsthenvisuallyverifiedusing12.5cmhighresolutionaerialphotos

§ Cellspreparedforinputastrainingandtestingpixels

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SelectionofSamplePoints

§ Goal:Twosetsoftrainingandtestingpixels,splitwitha75%-25%ratio§ 1)225trainingpixels,75testingpixelsforeachclass§ 2)750trainingpixels,250testingpixelsforeachclass

§ Filteringtoremovedatawherethereisoverlayoruncertaintyaboutwhichclassapixelbelongsto

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AlgorithmImplementation

§ UsingPython(Scikit Learn)allowedustoimplementrandomforestsandSVMandML.

§ Easytoimplement,user-friendly,nolicensingcost

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Results

TheresultsfromtheMLclassifier,onthe9bandimagewith225trainingpixelswasselectedasabaseline,becauseMLwasconsideredasthemostpopularclassifierinremotesensing.

MLprovedtoshowthelowestaccuracyresultswhencomparedtorandomforestsandsupportvectormachines.

Thebestaccuracywasachievedinfavorofthemostnumberofbands(9),trainingsites(750)andSVMalgorithm.

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Results– Baseline

§ MaximumLikelihood:9bands– 225trainingpixels

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Results:Bestclassifier

§ SupportVectorMachines:§ 9bands– 750trainingpixels

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Results:Weakclassifier

§ MaximumLikelihood:§ 4bands– 225trainingpixels

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Sentinel:opportunitiesandchallenges

§ Theimprovedspatial,spectral,radiometricandtemporalcoverageandalong-termoperationalcommitmentfromESAprovidesuswithanenormouscapacityforearthobservation.

§ Continuityofobservationisbecomingextreme.

§ CrosssensordatafusionwithinSentinelcanboostthequalityandtemporalityofourobservations.

§ AglobalperspectiveinearthobservationshouldbetakenintoaccountasmassiveareasinAfrica,middleeast,andAsiaarethreatenedduetoclimatechange.Asaresult,theentireglobeisatriskofoverexploitationofresources.

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Sentinel:opportunitiesandchallenges

§ CrossplatformapproachesforintegratingSentinelwithpre-Sentinelremotesensingdatae.g.,Landsatshouldbedevelopedinordertoachievelong-termobservations.

§ ToolsforSentineldata-to-productconversionforawidespectrumofusersshouldbedevelopedinordertominimizedataprocessingtime.

§ Now,wearelivinginrichfreedataeraalongwithsmartercitizensandubiquitousmobileapps,therefore,realtimeearthobservationcancometrue.

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Conclusions

§ ThestudyshowedthattheuseofmachinelearningalgorithmsonnewlyacquiredSentinel2imagerycanresultinrelativelyaccuratelandcover/landuseproducts.

§ Improvingtheaccuracyofthealgorithmsshouldbeafirstpriority.Thiscanbedonebyfurthertuningofthetrainingandtestingpixels,byfurthertuningofalgorithmparameters(e.g.moretreesinrandomforests),orbytheadditionofancillarydata(e.g. DEMs)

§ Oncegreateraccuracyisachieved,applyingthebestperformingalgorithmstoalargerareawithamorevariedgeographyforfurthertestingcanbebeneficial.

§ TheuseofSentinel2imageryhasthepotentialtoresultinbetterresolution,andpossiblymoreaccuratelandcover/landuseproducts.ForexampleincreasingtheresolutionofCORINEto10maswellasincludingmoretimestamps.

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FutureDirections

§ Improvementofaccuracyresultsthroughinclusionofimageindicesandancillarydata

§ Considerationofotheralgorithmse.g.deeplearning

§ ApplicationoftheapproachtolargerareaswithinEU

§ PotentialforimprovingCORINEandUrbanAtlasdataintermsofspatialresolution,temporalresolutionandmorerecentdatasets.

§ Lookingforwardtoseeingthefirstgloballandcoverproductat10m


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