Radar Remote Sensing of Urban Areas

286 Pages·2010·7.49 MB·English

Radar Remote Sensing of Urban Areas
Remote Sensing and Digital Image Processing
VOLUME15
SeriesEditor: EARSelSeriesEditor:
FreekD.vanderMeer AndréMarçal
DepartmentofEarthSystemsAnalysis DepartmentofAppliedMathematics
InternationalInstituitefor FacultyofSciences
Geo-InformationScienceand UniversityofPorto
EarthObservation(ITC) Porto,Portugal
Enchede,TheNetherlands
&
DepartmentofPhysicalGeography
FacultyofGeosciences
UtrechtUniversity
TheNetherlands
EditorialAdvisoryBoard: EARSelEditorialAdvisoryBoard:
MichaelAbrams MarioA.Gomarasca
NASAJetPropulsionLaboratory CNR-IREAMilan,Italy
Pasadena,CA,U.S.A.
MarttiHallikainen
PaulCurran HelsinkiUniversityofTechnology
UniversityofBournemouth,U.K. Finland
ArnoldDekker HåkanOlsson
CSIRO,LandandWaterDivision SwedishUniversity
Canberra,Australia ofAgriculturalSciences
Sweden
StevenM.deJong
DepartmentofPhysicalGeography EberhardParlow
FacultyofGeosciences UniversityofBasel
UtrechtUniversity,TheNetherlands Switzerland
MichaelSchaepman RainerReuter
DepartmentofGeography UniversityofOldenburg
UniversityofZurich,Switzerland Germany
Forothertitlespublishedinthisseries,goto
http://www.springer.com/series/6477
Radar Remote Sensing
of Urban Areas
Uwe Soergel
Editor
LeibnizUniversitätHannover
InstituteofPhotogrammetryandGeoInformation,Germany
123
Editor
UweSoergel
LeibnizUniversitätHannover
InstituteofPhotogrammetryandGeoInformation
NienburgerStr.1
30167Hannover
Germany
[email protected]
Coverillustration:Fig.7inChapter11inthisbook
ResponsibleSeriesEditor:AndréMarçal
ISSN1567-3200
ISBN978-90-481-3750-3 e-ISBN978-90-481-3751-0
DOI10.1007/978-90-481-3751-0
SpringerDordrechtHeidelbergLondonNewYork
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Preface
One of the key milestones of radar remote sensing for civil applications was the
launchoftheEuropeanRemoteSensingSatellite1(ERS1)in1991.Theplatform
carriedavarietyofsensors;theSyntheticApertureRadar(SAR)iswidelyconsid-
ered to be the most important.This active sensing technique providesall-day and
all-weather mappingcapability of considerablyfine spatial resolution. ERS 1 and
its sister system ERS 2 (launch 1995) were primarily designed for ocean appli-
cations, but soon the focus of attention turned to onshore mapping. Examples for
typical applications are land cover classification also in tropical zones and moni-
toringofglaciersorurbangrowth.Inparallel,internationalSpaceShuttleMissions
dedicated to radar remote sensing were conducted starting already in the 1980s.
ThemostprominentweretheSIR-C/X-SARmissionfocussingontheinvestigation
ofmulti-frequencyandmulti-polarizationSARdataandthefamousShuttleRadar
TopographyMission (SRTM). Data acquired during the latter enabled to derive a
DEM of almostglobalcoverageby meansof SAR Interferometry.Itis indispens-
ableeventodayandformanyregionsthebestelevationmodelavailable.Differential
SARInterferometrybasedontimeseriesofimageryoftheERSsatellitesandtheir
successorEnvisatbecameanimportantanduniquetechniqueforsurfacedeforma-
tionmonitoring.
The spatial resolution of those devices is in the order of some tens of meters.
Imageinterpretationfromsuch datais usuallyrestrictedto radiometricproperties,
which limits the characterization of urban scenes to rather general categories, for
example,thediscriminationofsuburbanareasfromcitycores.Theadventofanew
sensorgenerationchangedthissituationfundamentally.Systemslike TerraSAR-X
(Germany)andCOSMO-SkyMed(Italy)achievegeometricresolutionofabout1m.
In addition,these sophisticatedsystems are moreagileand provideseveralmodes
tailored for specific tasks. This offers the opportunity to extend the analysis to
individual urban objects and their geometrical set-up, for instance, infrastructure
elements like roads and bridges, as well as buildings. In this book, potentials and
limits of SAR for urban mapping are described, including SAR Polarimetry and
SARInterferometry.Applicationsaddressedcompriserapidmappingincaseoftime
critical events, road detection, traffic monitoring, fusion, building reconstruction,
SARimagesimulation,anddeformationmonitoring.
v
vi Preface
Audience
This bookis intendedto providea comprehensiveoverviewof the state-of-theart
of urban mapping and monitoring by modern satellite and airborne SAR sensors.
The reader is assumed to have a background in geosciences or engineering and
to be familiar with remote sensing concepts. Basics of SAR and an overview of
differenttechniquesandapplicationsaregiveninChapter1.Allchaptersfollowing
thereafterfocusoncertainapplications,whicharepresentedingreatdetailbywell
knownexpertsinthesefields.
In case of natural disaster or political crisis rapid mapping is a key issue
(Chapter 2). An approach for automated extraction of roads and entire road net-
worksispresentedinChapter3.Atopiccloselyrelatedtoroadextractionistraffic
monitoring.Incase ofSAR, Along-TrackInterferometryis apromisingtechnique
for this task, which is discussed in Chapter 4. Reflections at surface boundaries
mayalterthepolarizationplaneofthesignal.InChapter5,thiseffectisexploited
for objectrecognitionfrom a set of SAR imagesof differentpolarizationstates at
transmitandreceive.Often,up-to-dateSARdatahastobecomparedwitharchived
imageryofcomplementingspectraldomains.AmethodforfusionofSARandop-
tical images aiming at classification of settlements is described in Chapter 6. The
opportunitytodeterminetheobjectheightabovegroundfromSARInterferometry
is of course attractive for building recognition. Approaches designed for mono-
aspectandmulti-aspectSARdataareproposedinChapters7 and 8, respectively.
Such methods may benefit from image simulation techniques that are also useful
foreducation.InChapter9,amethodologyoptimizedforreal-timerequirementsis
presented.Monitoringofsurfacedeformationsuffersfromtemporalsignaldecorre-
lation especiallyin vegetatedareas. However,in cities manytemporallypersistent
scatteringobjectsarepresent,whichallowtrackingofdeformationprocesseseven
for periodsof severalyears. Thistechniqueis discussed in Chapter10. Finally,in
Chapter11,designconstraintsofamodernairborneSARsensorarediscussedfor
thecaseofanexistingdevicetogetherwithexamplesofhigh-qualityimagerythat
state-of-the-artsystemscanprovide.
UweSoergel
Contents
1 ReviewofRadarRemoteSensingonUrbanAreas....................... 1
UweSoergel
1.1 Introduction............................................................. 1
1.2 Basics................................................................... 2
1.2.1 ImagingRadar................................................ 3
1.2.2 Mappingof3dObjects....................................... 8
1.3 2dApproaches.......................................................... 11
1.3.1 Pre-processingandSegmentationofPrimitiveObjects..... 11
1.3.2 ClassificationofSingleImages.............................. 13
1.3.2.1 DetectionofSettlements.......................... 14
1.3.2.2 CharacterizationofSettlements.................. 15
1.3.3 ClassificationofTime-SeriesofImages..................... 16
1.3.4 RoadExtraction............................................... 17
1.3.4.1 RecognitionofRoadsandofRoadNetworks... 17
1.3.4.2 Benefit of Multi-aspectSAR
ImagesforRoadNetworkExtraction............ 19
1.3.5 DetectionofIndividualBuildings ........................... 20
1.3.6 SARPolarimetry ............................................. 20
1.3.6.1 Basics.............................................. 21
1.3.6.2 SARPolarimetryforUrbanAnalysis............ 23
1.3.7 FusionofSARImageswithComplementingData ......... 24
1.3.7.1 ImageRegistration................................ 24
1.3.7.2 FusionforLandCoverClassification............ 25
1.3.7.3 Feature-Based Fusion of
High-ResolutionData............................. 26
1.4 3dApproaches.......................................................... 26
1.4.1 Radargrammetry.............................................. 27
1.4.1.1 SingleImage...................................... 27
1.4.1.2 Stereo.............................................. 28
1.4.1.3 ImageFusion...................................... 29
vii
viii Contents
1.4.2 SARInterferometry .......................................... 29
1.4.2.1 InSARPrinciple .................................. 29
1.4.2.2 AnalysisofaSingleSARInterferogram ........ 32
1.4.2.3 Multi-imageSARInterferometry................ 34
1.4.2.4 Multi-aspectInSAR............................... 34
1.4.3 FusionofInSARDataandOtherRemote
SensingImagery.............................................. 36
1.4.4 SARPolarimetryandInterferometry........................ 37
1.5 SurfaceMotion......................................................... 38
1.5.1 DifferentialSARInterferometry............................. 38
1.5.2 PersistentScattererInterferometry........................... 39
1.6 MovingObjectDetection.............................................. 40
References...................................................................... 41
2 RapidMappingUsingAirborneandSatelliteSARImages ............. 49
FabioDell’AcquaandPaoloGamba
2.1 Introduction............................................................. 49
2.2 AnExampleProcedure................................................. 51
2.2.1 Pre-processingoftheSARImages .......................... 51
2.2.2 ExtractionofWaterBodies .................................. 52
2.2.3 ExtractionofHumanSettlements............................ 53
2.2.4 ExtractionoftheRoadNetwork............................. 54
2.2.5 ExtractionofVegetatedAreas ............................... 56
2.2.6 OtherSceneElements........................................ 57
2.3 ExamplesonRealData ................................................ 57
2.3.1 TheChengduCase............................................ 58
2.3.2 TheLuojiangCase............................................ 61
2.4 Conclusions............................................................. 64
References...................................................................... 66
3 FeatureFusion Based on Bayesian NetworkTheory
forAutomaticRoadExtraction............................................. 69
UweStillaandKarinHedman
3.1 Introduction............................................................. 69
3.2 BayesianNetworkTheory............................................. 70
3.3 StructureofaBayesianNetwork...................................... 72
3.3.1 Estimating Continuous Conditional
ProbabilityDensityFunctions ............................... 76
3.3.2 DiscreteConditionalProbabilities........................... 79
3.3.3 EstimatingtheA-PrioriTerm................................ 80
3.4 Experiments ............................................................ 81
3.5 DiscussionandConclusion............................................ 82
References...................................................................... 85
Contents ix
4 TrafficDataCollectionwithTerraSAR-X
andPerformanceEvaluation................................................ 87
Stefan Hinz, Steffen Suchandt, Diana Weihing,
andFranzKurz
4.1 Motivation.............................................................. 87
4.2 SARImagingofStationaryandMovingObjects..................... 88
4.3 DetectionofMovingVehicles......................................... 93
4.3.1 DetectionScheme ............................................ 94
4.3.2 IntegrationofMulti-temporalData.......................... 96
4.4 MatchingMovingVehiclesinSARandOpticalData................ 98
4.4.1 MatchingStaticScenes....................................... 98
4.4.2 TemporalMatching...........................................100
4.5 Assessment .............................................................101
4.5.1 AccuracyofReferenceData .................................101
4.5.2 AccuracyofVehicleMeasurementsinSARImages........103
4.5.3 ResultsofTrafficDataCollection
withTerraSAR-X.............................................103
4.6 SummaryandConclusion..............................................107
References......................................................................107
5 ObjectRecognitionfromPolarimetricSARImages......................109
RonnyHa¨nschandOlafHellwich
5.1 Introduction.............................................................109
5.2 SARPolarimetry.......................................................111
5.3 FeaturesandOperators.................................................117
5.4 ObjectRecognitioninPolSARData ..................................124
5.5 ConcludingRemarks...................................................129
References......................................................................130
6 FusionofOpticalandSARImages.........................................133
FlorenceTupin
6.1 Introduction.............................................................133
6.2 ComparisonofOpticalandSARSensors.............................135
6.2.1 Statistics.......................................................136
6.2.2 GeometricalDistortions......................................137
6.3 SARandOpticalDataRegistration ...................................138
6.3.1 KnowledgeoftheSensorParameters........................138
6.3.2 AutomaticRegistration.......................................140
6.3.3 A FrameworkforSARandOpticalData
RegistrationinCaseofHRUrbanImages ..................141
6.3.3.1 RigidDeformationComputation
andFourier–MellinInvariant.....................141
6.3.3.2 PolynomialDeformation .........................143
6.3.3.3 Results.............................................144
x Contents
6.4 FusionofSARandOpticalDataforClassification...................144
6.4.1 StateoftheArtofOptical/SARFusionMethods...........144
6.4.2 A Framework for Building Detection
BasedontheFusionofOpticalandSARFeatures .........147
6.4.2.1 MethodPrinciple..................................147
6.4.2.2 BestRectangularShapeDetection...............148
6.4.2.3 ComplexShapeDetection........................149
6.4.2.4 Results.............................................150
6.5 JointUseofSARInterferometryandOpticalData
for3DReconstruction..................................................151
6.5.1 Methodology..................................................151
6.5.2 ExtensiontothePixelLevel .................................154
6.6 Conclusion..............................................................157
References......................................................................157
7 Estimationof UrbanDSMfromMono-aspectInSAR
Images..........................................................................161
Ce´lineTisonandFlorenceTupin
7.1 Introduction.............................................................161
7.2 ReviewofExistingMethodsforUrbanDSMEstimation............163
7.2.1 ShapefromShadow ..........................................164
7.2.2 ApproximationofRoofsbyPlanarSurfaces................164
7.2.3 StochasticGeometry..........................................165
7.2.4 HeightEstimationBasedonPriorSegmentation ...........165
7.3 ImageQualityRequirementsforAccurateDSMEstimation ........166
7.3.1 SpatialResolution ............................................166
7.3.2 RadiometricResolution ......................................168
7.4 DSMEstimationBasedonaMarkovianFramework.................169
7.4.1 AvailableData ................................................169
7.4.2 GlobalStrategy ...............................................169
7.4.3 FirstLevelFeatures...........................................171
7.4.4 FusionMethod:JointOptimizationofClass
andHeight ....................................................172
7.4.4.1 DefinitionoftheRegionGraph ..................172
7.4.4.2 FusionModel:Maximum
APosterioriModel................................173
7.4.4.3 OptimizationAlgorithm..........................178
7.4.4.4 Results.............................................178
7.4.5 ImprovementMethod.........................................179
7.4.6 Evaluation.....................................................181
7.5 Conclusion..............................................................183
References......................................................................184