coastline detection and coastal zone classification with ... · pdf...

CoastlineDetectionandCoastalZoneClassificationwithSpaceborneSyntheticApertureRadarImagery

Xiaofeng LiNESDIS/STAR

Collaborators:F.Nunziata,UniversitàdegliStudidiNapoliParthenope, ItalyX.Yang,ChineseAcademyofSciences

Outline

CoastlineDetection:Single-polarizationSARimageryDual-polSARdata

CoastalZoneClassification:Full-polSARdata

Part1:CoastlineDetection

Motivations:• Acontinuousupdateofcoastalmapsisneeded

• Coastalaccretionanderosion• Cartography• Disastersmapping

Part1:Traditionaltools

Traditionalapproachestocoastlinemappingare:

• GeodeticsurveyandGPS-RTK:highaccuracybutlimitedcoverage

• Aerialphotography:continuoussurveybutexpensive• Airbornevideography/videosystems:continuous• collectionofdatabutexpensive• Conventionaltechniquesarenotcost-effective

Part1:Moderntools– OpticalSensor

OpticalSensors

• Cloudcover• Solarillumination• Meteorologicalconditions

Part1:Moderntools- SAR

Advantages:• Globalcoverage• Allweather• Day&night• Finespatialdetails• Easyimageinterpretation

Shortcomings:• Revisittime• Notavailable insomeareas

Maintechnicalissues• Speckle• Lackofsea/landcontrast• Complexity

Part1:Single-Pol SARcoastlinedetection

Land/seadiscrimination• Edgedetection• Segmentation

Coastlineextraction• Edgedetection.• Activecontourtracing

Partially unsupervised or supervised


Land/seadiscrimination• Edgedetection• Segmentation

Coastlineextraction• Edgedetection.• Activecontourtracing

Partially unsupervised or supervised

DiagramofwaterlineextractionfromSARimages


CSKVVMarch6,2012at05:31 3X3LeeFilter Multi-scalecut,threshold

Automaticedgetracing Automaticoverlay Manuallyrevisit


SixSARimagesatspring-tidetimes.TheacquisitiontimesofthesesixSARimagesfrom(a)to(f)are29November1993,19February1996,20February1997,17December1998,25May1999,16Nov1999,21December1999and22February2005.


Waterlinemovementduetothe landreclamationproject– oneofthe largestintheworld

Part1:Single-Pol SARcoastlinedetectionCaseStudyResults:

Theshorelinemovedsubstantiallyseawardfrom1993to2005.• 1993to1996,theshorelinemovedslowly<40m/year,naturalforces• 1996and1999,theshorelinemovedfast>190m/year,artificialimpellingsiltationand

thesiltintheYangtzeRiverdeposition• After1999,shorelinemovementbecamefasterinbotheastandwestsides,withan

averagespeedof390m/yand160m/yrespectively,tidalflatreclamationproject.

Summary:AnewmethodforwaterlineextractionfromSARimagesusingdivide-and-combineapproach

andmulti-scalenormalizedcutsegmentationwasimplementedinthisstudy.

OurresultsindicatetheproposedmethodwaseffectiveinwaterlineextractionfromERS-1/2andENVISATSARimageswithaccuracybetterthan100m.

Part1:Dual-Pol SARcoastlinedetection

Atwo-stepprocedureisproposedtoextractcoastlinebydual-pol SARdatathatcoverstheprocessingandpost-processingstepsofconventionaltechniques

1) PingPong HH/VVCSKSARdataforland/seadiscrimination

2) Simpleimageprocessingtoextractcoastline;


CSKPingPongmodeSpatialresolution15X15m.Swath30X30km.

PingPong modeimplementsastripacquisitionbyalternatingapairofTX/RXpolarizationsacrossburstsbymeansofanantennasteering

Thesignalpol isalternatedbetweentwopossibleones:VV,HH,HVandVH.

Thetimeoffsetbetweentwosuccessivebursts,tp,varies,accordingtothebeamtype,between0:15sand0:25s.


PingPongmodeactslikeanalong-trackinterferometer

Distinguishland/seasurface•Seasurfacescattering

ts <0:035s;tp >>tsrc =0

•LandscatteringAstrongerandmorepersistentbackscatteredsignalisexpected;tp <ts;Largerrc valuesareexpected;


Step2:

Smallisolatedtargetsarefilteredoutfromtherc binaryimage;

TwoGaussiankernelstoextractintermediatefrequenciesinthespatialdomain;

AnarrowregularizationFilterandabroaderLPfilterapplied.


Italy


Single-Pol:Bias:13.8m;STD=20.8m

Dual-Pol:Bias:26.0m;STD=66.8m

GPSstations

Part1:Summary

1) BothmethodsworkwellinshorelineextractionfromSARimagesintheintertidalflatunderlighttomoderatewindconditions.Thereasonisthatwhenthetidelevelishighandthewindisnotstrong,theboundarybetweenwaterandlandisclearinSARimages.

2) TheaccuracyofthewaterlineextractedfromCSKSARimagesbybothmethodsdecreaseswheretherewaswaterinthe intertidalflat.Thereasonisthattheboundary betweenwaterandwetintertidalflatisnotclearinSARimages.

3) Thesingle-polarizationmethodhasslightlyhigheraccuracyinwaterlineextractionfromSARimagesthanthedual-polarizationmethodduetoasupervisedpost-processing.Thedual-polarizationmethodhashigherefficiencyinwaterlineextractionfromSARimagesduetoautomaticprocessing.

• areintermediateareasbetweenthelandandseawithhighaccessibility;• hostinteractionsbetweenlandandoceansystems;• playcriticalrolesinregulatingglobalhydrologyandclimate,andchemicalmodifications;• provideremarkableproductivityandbiodiversity.

Coastalzonesmudflats aquiculturegrids

musselbeds mangroves

vegetateddunesandflats

Part2:CoastalZoneClassification

Naturalfactors• largespan,weatherandtidalconditions;• repetitivefloodingthatcausedangerous;• somewherehardtoaccessneitherbyboatnoronfoot.

Economicfactors• timeconsuming;•manpowerconsuming;•materialresourcesconsuming.

Part2:TraditionalTools

Opticalsensors• strong dependence on cloud conditions and daylight;• short timewindow around low tide (approx. 3h);• similar spectral characteristics;

• omission or underestimation of wetland areas coveredby forest, scrub, and grasses.

cloud

daylight

similarspectrum

Part2:ModernTools– OpticalSensor

• all-weathercapabilities;• independenceofdaylight;• penetratingcapacity;• obtainmentofdynamicinformationintheupperocean;• acquisitionofstructuralandelectromagneticinformation;• multi-temporal,multi-polarization,andmulti-frequency;• routineandaccuratemonitoringofintertidalflats.

SyntheticApertureRadar(SAR)

ALOS-1 Radarsat-1ALOS-2 Radarsat-2

Part2:ModernTools– SAR

TerraSAR EnviSAT SeaSATCosmo-Skymed

Part2:LandCovertypesincoastalzoneSediments• sandflats:sandytidalshoalswithabundantlinguoid ripples,sinuousandlinearmegaripples,andexposedatlowtide;• mudflats:coastalsanddunesformedbysandyquartzsedimentsafterbeingreworkedbywind;

Habitats• mangroves:densemangroveforestsovertidalmudflatsbetweenhighspringandmeantidelevels;

• musselbeds:bivalvesthatstickoutofsediments,increasinglocalsurfaceroughness;• wetland:coastalcoveragewithoutmanysedgesorrushes,butdominatedbygrasses;• transitionzones:supratidalmangrovesandmarshesthataretopographicallyhigher,alwayswithsmallermangrovesandwidelyspacedgrasses.

transitionzonesmusselbedsmangrovesmudflatssandflats wetland

Part2:ScatteringBehaviorofSedimentsandHabitats

Surfacescatteringdominant• mudflats;• sandflats;• rockyoutcrops;• peat;• woodydebris;…

Volumescatteringdominant• musselbeds;• humanmadematerials;• erodingtundra;• mangroves;…

Doublebouncescatteringdominant• lateriteblocks;• wetlandmeadow;…

1 Basedonbackscatteringmodels

Part2:ClassificationMethods

- multilooking- specklefiltering- geocoding

- NRCS- RMSheight- correlationlength

- landcovertypes- parametervectors

- coastalzoneclassification

pre-processing

parameterretrieval

relationshipbuilding

classification


Feasibility• from1to6,differentcovertypeslocatesbetweeneachstartingandendingnodes;

• differentcovertypesgivedifferentparameterpatterns;

Advantages• the retrieved parameters can be considered as proxies for sediment type;

• relatively high applicability;

• connection with scattering mechanisms.

Limitations• correlation lengths always contain errors mainly because of the partial form of theautocorrelation function used in calculations;

• the surface roughness are always underestimated because of the remained waterin the ripple troughs on the flats;

• the total NRCS are sensitive to the wind speed and local weather conditions;

• different model gives different results under different environmental condition;

• mainly suitable for natural media on intertidal flats.

Part2:ClassificationMethods——basedonbackscatteringmodels

2 Based on image transformation of multi-polarized SAR data


- multilooking- specklefiltering- colortransformation

- mathematicaltransformations

- landcovertypes- parametervectors

- coastalzoneclassification

pre-processing

polarimetric imagetransformation

relationshipbuilding

classification

Datapreparation• changecolorspace;• polarimetricintensitychannelsHH/HV/VH/VV;

Mathematicaltransformation• maketargetsofinterestvisible;• highlightscatteringdifferencesbetweenintensitychannels;

• connectnumeralparameterswithlandcoverclasses;

Classifiers• Minimizedisparitieswithinclasses;• Maximizedisparitiesbetweenclasses.


Feasibility• colorcomposite(R-HH,G-HV,B-VV);• mathematicaltransformation(ρcross，ρHHVV，ΦHHVV,…);• differentcovertypesbehavedifferentlyinmathematicalindicatorsbasedonoriginalchannels.


Otherindicators• intensityratio(DoP,meanintensity);• POINCAREsphere(PDor,IDap,ADap,…);• polarizationratio(ρcross，ρHHVV，ΦHHVV,…);


Each indicator gives unique characteristics that can highlight certain targets with uniquescattering patterns. Focusing on different objectives in classification, many classes can beextracted.

Advantages• object-oriented features as valuable input for existing classification system;

• not only focus on scatteringmechanisms, but also target structures;

• suitable for various land cover types by introducingpolarization channels.

Limitations• sensitive to environmental conditions;

• limited classification capability for certain land cover type on intertidal flats, such ascoastal sediment types subdivision;

• not fully exploit polarimetric information.

Part2:ClassificationMethods——basedonimagetransformation

3 Based on incoherent polarimetric decomposition theory


unsupervised

slanttogroundrange

multilooking

specklefiltering

polarimetricdecomposition

geocoding

trainingsample

classifier

classificationresults

featurecombinations

supervised

- pre-processing

- decomposition componentsextraction

- Classification- supervised- unsupervised

Decompositiontheory• based on wave dichotomy

• based on eigenvalues analysis

• based on physical scattering model


Advantages• fully exploit target structure and geophysical information;

• increase classification accuracy considerably;

• more intuitive to interpret thanbackscatter intensity channels;

• better suited for unsupervised SAR classification of intertidal flats .

Limitations• greater classification capability for land cover types on intertidal flats, such as coastalsediment types subdivision and mussel beds extraction;

• require full-polarization SAR data source which are limited available.

Part2:ClassificationMethods——basedonpolarimetricdecompositiontheory

Part2:ResearchExpectations

• SAR acquisitions are promising data source for classification of intertidalflats;

• Multi-polarization SAR acquisitions show large potentials for detectingstructure and geophysical information that always invisible in othertypes of SAR data.

• For the rapidly changing tidal flats, some land cover types are stilldifficult to distinguish or extracted with lower accuracies, which needsmore SAR information as ancillary.

• Multi-temporal and multi-frequency SAR acquisitions are anticipated todevelopmore information statistically from frequent- and time-series.

Part2:Summary

• DifferentcoastalzonetypescanbeclassifiedfromSARimagery;

• Needmorevalidationstudies;

• Shortcomingisthelimitedcoveragebyfull-polimagery(TerraSAR,ALOS-2,Cosmo-Skymed).

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