Monitoring Urban Areas with Sentinel-2A Data: Application to the Update of the Copernicus High Resolution Layer Imperviousness Degree

2016·45.4 MB·

by  Antoine Lefebvre

remote sensing
Article
Monitoring Urban Areas with Sentinel-2A Data:
Application to the Update of the Copernicus High
Resolution Layer Imperviousness Degree
AntoineLefebvre1,*,ChristopheSannier2,† andThomasCorpetti3,†
1 CNES,UMR6074IRISA,OBELIXTeam,Vannes56000,France
2 SIRS,Villeneuved’Ascq59650,France;[email protected]
3 CNRS,UMR6554LETGCOSTEL,Rennes35000,France;[email protected]
* Correspondence:[email protected];Tel.:[phone];Fax:[phone]
† Theseauthorscontributedequallytothiswork.
AcademicEditors:ClementAtzbergerandPrasadS.Thenkabail
Received:31March2016;Accepted:7July2016;Published:19July2016
Abstract: Monitoringwithhighresolutionlandcoverandespeciallyofurbanareasisakeytask
that is more and more required in a number of applications (urban planning, health monitoring,
ecology,etc.). Atthemoment,someoperationalproducts,suchasthe“CopernicusHighResolution
ImperviousnessLayer”,areavailabletoassessthisinformation,butthefrequencyofupdatesisstill
limiteddespitethefactthatmoreandmoreveryhighresolutiondataareacquired. Inparticular,the
recentlaunchoftheSentinel-2AsatelliteinJune2015makesavailabledatawithaminimumspatial
resolutionof10m,13spectralbands,wideacquisitioncoverageandshorttimerevisits,whichopens
alargescaleofnewapplications. Inthiswork,weproposetoexploitthebenefitofSentinel-2images
tomonitorurbanareasandtoupdateCopernicusLandservices,inparticulartheHighResolution
Layer imperviousness. The approach relies on independent image classification (using already
available Landsat images and new Sentinel-2 images) that are fused using the Dempster–Shafer
theory. Experimentsareperformedontwourbanareas: alargeEuropeancity,Prague,intheCzech
Republic,andamid-sizedone,Rennes,inFrance. Results,validatedwithaKappaindexover0.9,
illustratethegreatinterestofSentinel-2inoperationalprojects,suchasCopernicusproducts,andsince
suchanapproachcanbeconductedonverylargeareas,suchastheEuropeanorglobalscale. Though
classificationanddatafusionarenotnew,ourprocessisoriginalinthewayitoptimallycombines
uncertaintiesissuedfromclassificationstogeneratemoreconfidentandpreciseimperviousnessmaps.
Thechoiceofimperviousnesscomesfromthefactthatitisatypicalapplicationwhereresearchmeets
theneedsofanoperationalproduction. Moreover,themethodologypresentedinthispapercanbe
usedinanyotherlandcoverclassificationtaskusingregularacquisitionsissued,forexample,from
Sentinel-2.
Keywords: Sentinel-2;urbanareas;Copernicus;datafusion;imperviousness
1. Introduction
1.1. MonitoringUrbanAreaswithRemoteSensing: ACopernicusLandMission
Understandingtheurbangrowthphenomenonisamongthemajorissuesthatpublicserviceshave
todealwith. Today,morethanhalfoftheworldpopulationlivesinurbanareas,anditisestimated
thatthiswillreachuptotwo-thirdsby2025[1]. Inthecontextofglobalizationandclimatechange,
notonlytheurbanizationprocessisfast,buttheconsequencesduetotheincreaseofimperviousness
posesseriouschallengesrelatedtotheindicationofrisks(floods,heatwaveevents,pollution,etc.).
RemoteSens.2016,8,606;doi:10.3390/rs8070606 www.mdpi.com/journal/remotesensing
RemoteSens.2016,8,606 2of21
InEurope,theEuropeanCommissionconductsprojectstomonitorurbanareasinordertoserve
regionalfundingandpolicymaking. Amongthose,theHighResolutionLayerImperviousnessDegree
(HRLIMD),aswellastheUrbanAtlas,whicharepartoftheCopernicusLandmissions,arebasedon
remotesensingdata. OneoftheobjectivesoftheCopernicusprogramdevelopedbytheEuropean
Commissionistoprovideinformationproductsformonitoringthegrowthofurbanareasinorderto
assesswhetherpoliciestoreduceurbansprawlareeffective.
Amongthose,theHRLIMD,aswellastheUrbanAtlas(http://land.copernicus.eu/)arekey
products based on remote sensing data. The strength of these Copernicus Land products relies
ontheuseofhighresolution(spatial/temporal)remotesensingdata,whichprovidehomogeneity,
repetitivenessandobjectivityoverthewholeofEurope,whereascompilingavailablelocaland/or
nationaldatasetsthroughoutEuropeinordertocomparecitieswouldbedifficulttoachievesincethe
datawouldoriginatefromdifferentmaterialsandmethods.
TheHRLIMDconsistsofa20×20mgridcoveringallofEurope,includingTurkey,andaims
at representing the degree of imperviousness within each grid cell from 0% to 100%. This layer
hadalreadybeenproducedin2006,2009and2012andiskeyformonitoringurbansprawl. The2015
updatewillsoonbeproducedandwillcoincidewithanunprecedentedcoverageofremotesensing
data that can be used as input for its production. Previous updates were primarily based on a
semi-automatedapproach(mainlyduetothelimitedavailabilityofinputdata),andtheincreased
availabilityofremotely-senseddatacanincreasethelevelofautomationtoproduceandupdatesucha
largedataset.Inaddition,becauseofcloudcoverageinalargepartofEurope,thisamountofimages
canensurecloud-freeanduniformresultsataspatialresolutionlessthanorequalto20mtofitthe
HRL IMD specification. Large datasets are also critical to characterize vegetation phenology false
positivestoreducepotentialconfusionbetweeninducedagriculturalbaresoilandartificialsurfaces.
Allofthesecontributionsexpectedwiththeuseofmanydatarequirethedesignofinnovativeand
efficientmethodstoprocesssuchanamountofdatainordertomaintainCopernicusLandproducts’
dataproductioncostsdown.
Thesubstantialincreaseinremotely-senseddataavailabilityisprimarilyduetothelaunchof
Sentinel-2Ain2015,whichwasdesignedspecificallytomeettheneedsoftheCopernicusprogram.The
fullSentinel-2constellation(includingSentinel-2Aand-2B)willhavearevisitcycleoflessthanfive
daysglobally(whereasLandsat-8isaround16days)andwillprovide13bandsfromvisibletoShort
WaveInfrared(SWIR)associatedwiththreespatialresolutionsfrom10to60m. Themulti-temporal
resolution ensures a better monitoring of land use and cover with better opportunities to obtain
cloudlessmosaics;thewidespectralresolutionfacilitatesthethematicidentificationoflandcover[2],
whilethehighspatialresolutionallowsfortheidentificationofsmallobjects,suchasindividualhouses
orlandscapesstructures[3].
However,becausethelaunchofSentinel2Aisveryrecent,fewstudiesexistonhowitcouldbe
usedformonitoringurbanareas[4]. Therefore,inthispaper,amethodbasedonexploitingSentinel-2’s
unique properties is proposed to update the Copernicus High Resolution Imperviousness Layer.
However,thelargeamountofdatageneratedraisesmethodologicalquestionstodealwithreliable,
uncertainandcontradictoryinformation. Theseissuesarediscussedbelow.
1.2. UpdatingCopernicusProducts: ChangeDetectionandDataFusion
Twomainapproachesarepossibletodetectstructuralchangesinremotesensingdata: directly
detect changes from a pair of raw data or performing single classifications that are compared to
highlightalteredregions. Thefirstapproachisoftenpreferredsinceasingleprocesshastheadvantage
ofpreventingerrorsfrombeingaccumulatedthroughthetwoclassificationsteps. However,inthe
contextofthe“online”updatingofurbanareaswhereclassifiedregionsinthefirstimagearealready
available,thecomparisonwithanewclassificationissuedfromthenewimageappearsmorerational.
Thisistheprocesschoseninthispaper.Here,inordertopreventfromtheaccumulationorerrorsissued
RemoteSens.2016,8,606 3of21
fromindependentclassifications,weproposetoaccuratelyperformthisstepusingspecificdatafusion
rules. Beforedescribingourapproach,wefirstreviewbrieflythemainchangedetectionapproaches:
• Image to image comparison: A naive way to detect changes is to rely on differences, either
computed from the input images or on features derived from the images. Relying on the
simpledifferenceofrawdataislimited(becauseofknownproblemsrelatedtodifferencesin
terms of acquisition), and a substantial number of techniques has been proposed to provide
reliablemeasurements[5–8],forexamplestatisticalhypothesistests[9]orstatisticsonthespatial
relationsbetweenpoints[10,11]. Fromthefeaturedifferences,thedetectionofchangescanalso
beperformedusingsupervisedlearningtechniques[12].Thereadercanreferto[13]formore
detailsonassociatedapproaches.
• Post-classificationcomparison: Classificationplaysakeyroleinchangedetection, asitoften
enablesonetorefinetheresults. Strategiesrangefromtheclassificationofthevariousobjects
intheimages[14–16](inthiscase,thechangeisimmediatebycomparisonofthetwoclassified
data) to post-classifications or to classification of the change features [17–19]. As for the first
situation,thequalityoftheresultisstronglyrelatedtothesegmentation. In[20],theauthors
havefoundthatpost-classificationusuallyunderestimatestheareasoflandcoverchange,but
wherethechangewasdetected,itwasoverestimatedinmagnitude. Therefore,classifyingchange
features seems a more appealing choice. In that context, one can mention the work of [21],
wheretheauthorsuseanunsupervisedclassificationmixedwithavisualinterpretationofaerial
photographstodetectlandcoverchanges. Wereferthereaderstothecompletereviewsin[22–25]
foranoverviewofthechangedetectionissue.
Though well performing techniques exist, only a few of them specifically take into account
theproblemofhandlingaccuratelytheerrorsissuedfromindependentclassifications. In[26],the
authorsproposetofusetheclassificationresultsinordertominimizetheomissionorthecommission
errors. The approach introduced in this paper relies on the same idea. More precisely, we use the
Dempster–Shaferfusiontheory,sincethisframeworkisespeciallyadaptedtodealwithuncertainty
that can be derived according to previous classification performances (this theory is presented in
Section3.4). Inaddition,ourprocessdealswithcloudcoverage,enablingacontinuousgrowthofthe
areacoveredbyourapproach.
Classifications of single remote sensing images generally contain errors due to constraints
originatingfromboththedataandthealgorithmsused. Thisinaccuracy(oruncertaintyassociated
withclassificationresults)dependson:
1. thecharacteristicsofthesensor:
• itsspatialresolution(pixelscontaindifferentratiosofpureandmixedmaterials)
• itsspectralresolutionandtheacquisitiondate;
2. theefficiencyoftheclassification. Thoughingeneralasimplealgorithmsuchasthemaximum
likelihoodcanbeexpectedtobelessefficientthanamorecomplexone,suchasSupportVector
Machine(SVM)orRandomForest(RF),nogeneralrulecanbeextracted;
3. theaccuracyofthelabeleddatausedtotrainandtesttheclassifiers.
By taking into account all images issued from existing satellites, a large number of data can
be acquired on a given area. However, because of the different nature of associated images, their
classificationmayprovideresultsassociatedwithvariouslevelsofquality. Althoughselectingthe
bestresultamongallavailableclassificationswouldseemarationalapproach,combiningthemby
takingintoaccounttheirqualitiesshouldmakeitpossibletoreachahigherlevelofaccuracy. Thisis
theideabehindtheconceptofdatafusion. Alargenumberoftechniquesisavailabletofusedata,and
groupingthemintofamiliesisadifficulttask. Nevertheless,onecanroughlydistinguishthetwomain
groupsoftechniquesbasedon:
RemoteSens.2016,8,606 4of21
• probabilitytheory,whichaimsatdeterminingthestateofavariablegivenvariousobservations.
For these families, we mainly find the Kalman filter and other data assimilation techniques
dependingonthepresenceofmodelsforsensors(see,forexample,[27,28]);
• evidence theory, where each decision is represented with a belief function associated with
uncertainties(orinsomeapproaches,aparadox).Inthesefamilies,wefindDempster–Shafer
Theory(DST)anditsvariants[29]ortheDezert–Smarandacheparadoxicalapproach[30].
Inaremotesensingcontext,theDempster–ShaferTheoryhasmainlybeenappliedtofacilitate
the decision process for the classification in urban environments [31,32], but also in other various
contexts(seabreezefrontidentification,forestrymonitoringormoregenerally,landcover[33–36]).
Thoughwidelyusedinremotesensingdata,theDSThasonlyrarelybeenappliedinthecontextof
largedatasetclassification(suchasacountryorcontinent)whereinformationavailabilityissuesare
critical(intermsofrepetitiveness,cloudcover,acquisitionwindow,etc). Aswillbeshownlater,this
ideaisexploredinthispaperwhere,byfusingindividualclassificationsacquiredatdifferentdates,
urbanclassificationsaregeneratedcombiningbothanaccuracygreaterthantheindividualonesand
cloud-freecoverage. ItisalsoproposedtocombineSentinel-2withLandsat-8imagesandtoevaluate
thepotentialofourapproachasamulti-sourcefusion.
1.3. HighlightsofthePaper
Inthispaper,itisproposedtoexploitSentinel-2datatodesignafully-automatictechniquethat
updatesimperviousnessoverEuropebyensuring:
• amoreprecisequalitythanexistingproducts;
• anassociateddegreeofconfidence;
• lesssensitivitytocloudcoverage.
Aswillbeshowninthispaper,thegainintermsofqualityisduetotheuseofDempster’sfusion
rulethataccuratelydealswithuncertaintiesissuedfromallclassifications. Italsoenablesonetogivea
degreeofconfidencerelatedtoallestimations. Asforthelastpoint,becauseofthecloudcoverageall
overmemberstates’territories,theproductionofcloud-freedatarequiresahugeamountofimages
toensureuniformresultsataspatialresolutionlessthanorequalto20mtofitthespecificationof
Copernicusproducts.
2. StudyAreasandData
2.1. StudyAreas
Wechoosetovalidateourapproachesontwocitiesofdifferentsizeswithdifferenturbanplanning
policies:Prague(overonemillioninhabitants,6900km2),thecapitaloftheCzechRepublic,andRennes
(over200,000inhabitants,2500km2),amid-sizedcityinFrance. Theareasaredelimitedbythecontour
oftheCopernicusFormerUrbanAreas(FUA)oftheUrbanAtlasdatasetandarevisibleinFigure1.
Inpractice,theirinternalorganizationisdifferent: whileRennesismainlycomposedofmedium-sized
buildings,individualhousesandindustrial/commercialareas,Pragueislargerandembedsalarge
historicalcenterconnectedwithbigavenuestogetherwitholdandhighlevelapartmentsandsome
modernskyscrapersinitsperiphery. Therefore,thesecharacteristicsarecomplementaryandillustrate
thevarietyofsituationsoneencountersinpractice.
RemoteSens.2016,8,606 5of21
2 1
1. Prague, Czech Republic 20 km 2. Rennes, France 20 km
(Urban Atlas 2012) (Urban Atlas 2012)
Figure1.Presentationofthestudyareas.
2.2. Data
WetookapairofSentinel-2imagesforeachstudysite,thetimeshiftbetweenthetwoacquisitions
beingaboutaweek. Thisistooshorttoobservesignificantvegetationchanges,butthesetwoimages
remaininterestingtoovercomegapfillingissues(mainlybecauseofclouds).
TosimulatemorecompleteseriesofSentinel-2,wealsoaddedsomeLandsat-8images,sincethis
sensorisatthemomentthemostsimilarintermsofspecifications(spectralandspatialresolution).
Landsat-8 has 11 bands from coastal blue to middle infra-red and has spatial resolution varying
from 15 m to 60 m. Images acquired in spring (or early summer) and autumn were selected. All
characteristicsofthedataarevisibleinTable1.Thistimeperiodismoresuitabletotakeintoaccount
differentseasonalagriculturalpracticesandthereforetoevaluatetheabilityofourapproachtodetect
urbanchangeswhilebeinginsensibletoagriculturalones. TheseLandsatimagesarefirstusedasa
comparisonwiththeSentinel-2dataset. Inasecondstep,theyareincludedintheclassificationfusion
processtoassessthepotentialitiesofSentinel-2inamulti-sensorapproach.
Table1.Listofinputimages.
# Prague # Rennes
1 12/10/2015-Landsat8 1 09/09/2015-Landsat8
2 30/08/2015-Sentinel-2A 2 28/08/2015-Sentinel-2A
3 13/08/2015-Sentinel-2A 3 21/08/2015-Sentinel-2A
4 17/07/2015-Landsat8 4 11/05/2015-Landsat8
3. Method
Theoverallapproachiscomposedofsixsteps:
1. Datapreparation: extractingthecloudmaskandtexturedescriptorsofallimages
2. Superviseddatalearning: sampledatafromallimagesandbuildclassificationmodels
3. Mapproduction: classifyeachdatumfrommodelsofthepreviousstage
4. Classificationfusion: combineallclassifications
5. Changedetection: detectchangesontemporaldata
6. Accuracyassessment: validateourproducts
TheworkflowispresentedinFigure2,andeachstepisdetailedinthefollowingssections.
RemoteSens.2016,8,606 6of21
Reference Data
Input Image t
n t
0
1. Data Preparation 2. Supervised
Learning
Sample
Selection
Training sample
Cloud Texture Feature
Detection Extraction
Training
Cloud mask,
Texture Feature
Local uncertainty
Classification
Model
3. Map production
OA, K, Global
Classification Map tn Validation Uncertainty
4. Classification Fusion
Data Fusion
for t >= 2
n
Final Map,
Uncertainty
5. Change Detection 6. Accuracy Assesment
Post-classification Sample Selection
Training sample
comparison + CAPI
Change
Change Map Validation
detection OA, K
Figure2.Blockdiagramoftheworkflow.Differenttimestepsarenotedt0...tn;OAstandsfor“Overall
Accuracy”;Kisthekappaindex;CAPIstandsforComputer-AssistedPhotoInterpretation.
3.1. DataPreparation
The data preparation step consists mainly of two operations: cloud mask detection and
computationofthetexturedescriptorforeachimage.Astheyareclassifiedindependently,nospecific
spectralprocessing,norradiometriccorrectionsarerequiredoneachdatum.
RemoteSens.2016,8,606 7of21
3.1.1. CloudMaskDetection
Weperformclouddetectioninasemi-automaticwaythroughk-meansclassificationonBand1
(coastalblue),Band8A(near-infrared)andBand10(watervapor),whicharethemostinterestingto
highlightcloud. Becauseofthedifferencesinthesebands,cloudsandshadowsfallindistinctclusters
thataremanuallylabeledascloudsorshadows. Here,weperformamanuallabelingsincewehave
fewimages,butinanoperationalcontext,automaticmulti-temporalapproachescouldbeused,suchas
MTCD(Multi-TemporalCloudDetection[37]). Theirfinalshapesaredelineatedafteramorphological
openinginordertopreventfrombordereffects.
This method has the advantages of being simple, not requiring atmospheric correction and
providingefficientresults. AnexampleinFigure3presentstheresultofthecloudmaskdelineation
overPrague. Eventhoughthecloudopacityislow,weobserveinthisimagethatcloudsareaccurately
detected.
Near-infrared, Red, Green 2km Cloudmask 2km
(a) (b)
Figure 3. Example of cloud mask detection on a Sentinel-2 image: (a) original Sentinel-2 image;
(b)detectedcloudmask.
ConcerningLandsat-8images,cloudmasksweredirectlydownloadedfromtheUSGSservers.
These cloud masks rely on the Fmask (Function of mask) method, which is dedicated to Landsat
missionsandextractsbothcloudsandshadows[38].
3.1.2. ComputationoftheTextureDescriptor
To characterize the content of high or very high resolution remote sensing images, texture
descriptorsarepowerful,especiallyinurbanareas,asprovenbynumerousstudies[39–44].Amongthe
existingtechniques,theso-calledPANTEXmethod[44],whichreliesontheanalysisoftheGreyLevels’
CoocurrenceMatrices(GLCM)andthecomputationofassociatedindexes[45],hasthepropertiesof
beingsimpleandefficient. Tobeinvariantbyrotationandlesssensitivetosmalledgesthatappearin
ruralornaturallandscapes,PANTEXkeepstheminimumvalueoftheenergycomputedonGLCMin
variousdirections(10inpractice[44]). Thisenablesonetohighlightlocalcontrastvariation,which
isaparticularlycrucialcriteriainanurbanarea. PANTEXhasalreadyprovenitsefficiencyinurban
areas at a global level [46], and an example of PANTEX criteria is visible in Figure 4. In practice,
PANTEXcriteriaarecomputedintheblueband(10-mresolution)inthecaseofSentinel-2andinthe
panchromaticone(15mresolution)withLandsat-8.
RemoteSens.2016,8,606 8of21
Near-infrared, Red, Green Pantex intensity
(a) (b)
Figure4.PANTEXcriterioncomputedonSentinel-2image:(a)originalSentinel-2image;(b)minimum
PANTEXvalue.Asonecanobserve,localcontrastsissuedfromurbanareasareaccuratelyextracted.
3.2. SupervisedDataLearning
Thissuperviseddatalearningstepaimsatsamplingdataandbuildingaclassificationmodelper
imagethatwillbeappliedinthefurtherstep(Section3.3). Twoclasses,urbanareasandtherestofthe
landscape,areforeseenintheclassificationoutput. Toextractlearningpointsinsideurbanareas,we
usetheCopernicusHighResolutionLayerImperviousnessDegree2012(HRLIMD2012)asareference.
Itisindeedassumedthaturbanareasin2012arestilllabeledasurbanregionsinthefuture. Indeed,
urbanareasgrowslowlyandonsomesmallareascomparedtotheentirearablelandavailable. More
quantitatively,giventhaturbanareasrepresentjustover1%oftheoveralllandinEuropeandthey
grewbyabout0.6%peryearbetween1990and2000[47],theprobabilityofsamplingchangesremains
below2%. Changesselectedinarandomsamplingcanthenbeconsideredasnoiseinthedatasource
andshouldbeneglected. Thisenablesonetoprovidealargenumberofsamplesovertheentirearea
onwhichaclassificationmodelcanbedesigned.
Inpractice,amaximumsamplingof1,000,000pixelsovertheimageisperformed. Thenumberof
samplesisnotequalforeachclass,butdependsontheirproportionintheimage. Forexample,given
thattheurbanareasrepresentabout4%oftheimage,thedistributionofsamplesisabout4%inthe
‘urbanarea’classand96%forthe‘non-urban’one. Thisismoreadaptedtorepresentthedifferent
spectralcharacteristicsoftherurallandscape’sclasses(vegetation,baresoil,water,forest).
Theclassificationalgorithmusedforthisapplicationisbaggingtrees[48],whichisnotsensitiveto
alargenumberoftrainingdata. Baggingispartoftheensemblemethods,whichalsoincluderandom
forestandboosting. Ensemblemethodshavealreadyproventheirefficiencyfortheprocessingof
remotesensingimages,inparticularinurbanenvironments[44,49]. Theyconstituteasetoftechniques
forimprovingtheperformanceofsimpledecisiontrees. Theglobalideaistoperformmultiplerandom
samplingsandtoconstructseparatetreemodels. Eachtreeprovidesacriterionrelatedtothefactthat
theinputdatabelongtotheurbanclassornot. Then,achoiceprocedureonthebasisofeachresultis
applied(averageingeneral).
Thoughmanyotherclassificationtechniquescouldbeused,baggingremainsasuitablechoiceto
deal with a large number of observations (as well as neural networks, for example), since their
calibration is fast. Moreover, with such an approach, it is also possible to easily evaluate the
discrimination power of each input variable by evaluating their impact on each node, which is
ofprimeimportancetoassessthediscriminativepowerofSentinel-2bands[2].
AllspectralbandsarekeptinthefeaturevectoronwhichweaddPANTEXcriteria. Featuresare
thenresampledbynearest-neighborsat(20×20m)withrespecttothespecificationsoftheCopernicus
IMDHRLproduct.
RemoteSens.2016,8,606 9of21
3.3. MapProduction
Eachimagehasbeenclassifiedusingitsownbaggingtreemodelandvalidatedbycross-validation.
Therefore,ineachpixel(x,y),aseriesofcriteriaC(x,y,t)(correspondingtotheconcatenationofthe
resultsofallmodelsforeachtimet)thatbelongstotheurbanthematicclassisavailable. Aglobal
qualitycriterioncanthenbeestimatedtoevaluateitsaccuracyandprovideanoveralluncertaintyfor
eachdate. Basedonthekappaindex,theglobaluncertainty∆ (t)attimetiscomputedas:
global
∆ (t) =1−k(t) (1)
global
wherek(t)isthekappaindexoftheclassificationattimet. Withthiscriterion,agoodclassification
willgeneratelowglobaluncertaintyandviceversa. Thisensembleofcriteriaassociatedwiththeir
globaluncertaintyneedsnowtobefusedineachpixeltogenerateaglobalmap. Thisisthescopeof
thenextsection.
3.4. ClassificationFusion
Inthisstep,eachpixelcontainsavaluerelatedtoitssimilaritytotheurbanclassassociatedwitha
globaluncertainty. Wenowneedtosynthesizethisinformationtoprovideauniquemap. Tothisend,
werelyonevidencetheoryandinparticularontheDempster–ShaferTheoryofevidence(DST).The
DSTisbasedonaBayesianapproachandfusesasetofmassfunctionsissuedfromvarioussourcesof
observationsassociatedwiththebeliefonsomehypotheses.Akeyadvantageisthatuncertainty(union
ofhypotheses)isaccuratelymanagedwiththeDempster’sfusionrule. Appliedtoourclassification
problem, let U and nU bethehypothesesthatagivenpixelbelongstotheurbanclassornot. The
uncertainty is formalized as the union U ∪nU. For each time step t, if one computes three belief
functionsmU,mnU andmU∪nU,suchthatmU +mnU +mU∪nU =1and:
t t t t t t
1. mU isthebeliefthatthecorrespondingpixelbelongstotheurbanclass;
t
2. mnU isthebeliefthatthecorrespondingpixeldoesnotbelongtotheurbanclass;
t
3. mU∪nU isoveralluncertainty;
t
itispossibletocombinetwosourcesofinformationattimest andt (associatedwithmassfunctions
1 2
{mU,mnU,mU∪nU}and{mU,mnU,mU∪nU},respectively)usingDempster’sfusionrule. Thisisgiven,
t1 t1 t1 t2 t2 t2
byassumptionH ∈ ΘofthepowersetΘ = {U,nU,U ∪nU},by:
∑ m (A)m (B)
t1 t2
A,B∈Θ2,s.t.A∩B=H
m(H) = [mt1 ⊕mt2](H) = 1− ∑ m (A)m (B) (2)
t1 t2
A,B∈Θ2,s.t.A∩B=∅
(cid:124) (cid:123)(cid:122) (cid:125)
K
Roughly,thenumeratorcombinesthe4informationofthetwosourcesm andm inhypothesis
t1 t2
H,whilethedenominatorK =1− ∑ m (A)m (B)isrelatedtotheconflict(itssecond
t1 t2
A,B∈Θ2,s.t.A∩B=∅
termis0ifsourcesareinaccordanceandgrowwithconflict). Asthisfusionruleisassociative,to
combineNsourcesofinformation,westartbyfusingthetwofirstones,thenfusingtheresultwith
(cid:104)(cid:2) (cid:3) (cid:105)
thethirdone,andsoon(thisreads: m (H) = [m ⊕m ]⊕m ...⊕m (H),andonlypairwise
f t1 t2 t3 tN
fusionsdefinedinEquation(2)areinvolved.).
To apply this fusion rule to the classification of urban patterns, we need to define the mass
functions mU, mnU and mU∪nU ateachtime t. Letusremindthat,aftertheclassificationstep,each
t t t
pixelcontainsineachtimetavalueC(x,y,t)relatedtoitssimilaritytotheurbanclass. Inadditionto
thisvalue,aglobalaccuracy∆ (t)(seeEquation(1))isalsoavailableineachtimestep.
global
RemoteSens.2016,8,606 10of21
ThewaywegetmU(x,y),mnU(x,y)andmU∪nU(x,y)in(x,y)isthereforeanormalizationstep:
t t t
mUt (x,y) = C(cid:16)(x,y,t)/S(t)(cid:17)
S(t) =1+∆ (t) and mnU(x,y) = 1−C(x,y,t) /S(t) (3)
global t
mU∪nU(x,y) = ∆ (t)/S(t)
t global
In practice, to accurately deal with clouds, each pixel identified in Section 3.1 as a cloud has
uncertaintymU∪nU equalto1.
t
Finally, once fused, the final decision of the classification between urban/non-urban areas is
chosenasthemaximumbetweenmU andmnU ineachpixel(x,y)(withm thefusedmassfunctions).
f f f
3.5. ChangeDetectionandImperviousnessDegreeComputation
Eachtimeanewdatumisavailable,achangedetectioncanbeperformedbythecomparisonof
classifications.Thisenablesonetoeasilyupdatedatabases,aswillbeshownintheexperimentalparton
theCopernicusHRLIMD2012,whichhasbeenvalidatedbytheEEA(EuropeanEnvironmentAgency).
Thederivationofimperviousnessdegree(1%to100%)isproducedusinganautomaticalgorithm
basedonacalibratedNormalizedDifferenceVegetationIndex(NDVI).AdescriptionoftheCopernicus
ImperviousnessLayermethodologywasdescribedby[50]forthe2009updateandby[51]forthe2012
update. SimilarmethodswerealsoappliedintheUSAforthedevelopmentoftheNationalLand
Coverdatabase[52].
3.6. ChangeDetectionValidation
Change detection validation is made from a stratified sampling on the change stratum and
unchangedstratum.
A density threshold of 30% was used to derive the built-up layer from the imperviousness
layer[53].Thiswasnotintendedtobeaseparateproduct,butinsteadwascalculatedfortheverification
processonly,becausedensityproductscannotbeverified.
Basedontheassumptionthaturbanizedareasspreadmorethantheyappearrandomlyinthe
landscape,samplingunchangedareaswasdoneina250-mbufferzonealongurbanizedareas.
Inpractice,wefollowthesameprocessastheCopernicusproductstovalidateourresults[54]:
200 samples were selected including 100 samples in the change stratum and 100 samples in the
unchangedstratum. Eachsamplerepresentsa100×100mpolygoninwhichamanualinterpretation
wasperformed. Theassignedclassmatchesthemajorlandusesurfaceinsideapolygon.
4. ResultsandDiscussion
The overall processing chain presented in this paper has been applied on the Sentinel-2 and
Landsat-8imagesforthetwostudyareas. Independentclassificationshavebeenperformedineach
image,andthefusionhasbeenappliedintwodifferentways:
1. Multitemporalfusion: classificationsissuedfromimagesofthesamesensorhavebeenfused.
Thisyieldstworesults: fusionwithSentinel-2andfusionwithLandsat-8;
2. Multi-sourcefusion: allclassificationshavebeenfusedtoprovideasingleclassification.
ResultsaredepictedinFigures5and6forPragueandRennes, respectively, andquantitative
criteriaarepresentedinTables2and3,bothforindividualclassificationsandfusionsofthem. Because
of cloud coverage, results cannot be provided in each pixel, and we also show in these tables the
percentageofdataforwhichestimationsareavailable.
From these table, itis worth noting that overall accuracies andkappa indexes are good in all
situations. ThisfirstrevealstheabilityofourtextureindexbasedonPANTEXtoaccuratelyextract
urbanareas. However,lookingatindividualclassifications,weobservethatoverallurbanareasare