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Radar Remote Sensing of Urban Areas 2010 ed. [Pehme köide]

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  • Formaat: Paperback / softback, 278 pages, kõrgus x laius: 235x155 mm, kaal: 456 g, 120 Illustrations, black and white; XVI, 278 p. 120 illus., 1 Paperback / softback
  • Sari: Remote Sensing and Digital Image Processing 15
  • Ilmumisaeg: 05-May-2012
  • Kirjastus: Springer
  • ISBN-10: 9400731728
  • ISBN-13: 9789400731721
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Radar Remote Sensing of Urban Areas 2010 ed.Suurem pilt
  • Formaat: Paperback / softback, 278 pages, kõrgus x laius: 235x155 mm, kaal: 456 g, 120 Illustrations, black and white; XVI, 278 p. 120 illus., 1 Paperback / softback
  • Sari: Remote Sensing and Digital Image Processing 15
  • Ilmumisaeg: 05-May-2012
  • Kirjastus: Springer
  • ISBN-10: 9400731728
  • ISBN-13: 9789400731721
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9789400731721.html
Märksõnad:
One of the key milestones of radar remote sensing for civil applications was the launch of the European Remote Sensing Satellite 1 (ERS 1) in 1991. The platform carried a variety of sensors; the Synthetic Aperture Radar (SAR) is widely cons- ered to be the most important. This active sensing technique provides all-day and all-weather mapping capability of considerably ?ne spatial resolution. ERS 1 and its sister system ERS 2 (launch 1995) were primarily designed for ocean app- cations, but soon the focus of attention turned to onshore mapping. Examples for typical applications are land cover classi cation also in tropical zones and mo- toring of glaciers or urban growth. In parallel, international Space Shuttle Missions dedicated to radar remote sensing were conducted starting already in the 1980s. The most prominent were the SIR-C/X-SAR mission focussing on the investigation of multi-frequency and multi-polarization SAR data and the famous Shuttle Radar Topography Mission (SRTM). Data acquired during the latter enabled to derive a DEM of almost global coverage by means of SAR Interferometry. It is indispe- ableeventodayandformanyregionsthebestelevationmodelavailable. Differential SAR Interferometry based on time series of imagery of the ERS satellites and their successor Envisat became an important and unique technique for surface defor- tion monitoring. The spatial resolution of those devices is in the order of some tens of meters.
1 Review of Radar Remote Sensing on Urban Areas
1(48)
Uwe Soergel
1.1 Introduction
1(1)
1.2 Basics
2(9)
1.2.1 Imaging Radar
3(5)
1.2.2 Mapping of 3d Objects
8(3)
1.3 2d Approaches
11(15)
1.3.1 Pre-processing and Segmentation of Primitive Objects
11(2)
1.3.2 Classification of Single Images
13(1)
1.3.2.1 Detection of Settlements
14(1)
1.3.2.2 Characterization of Settlements
15(1)
1.3.3 Classification of Time-Series of Images
16(1)
1.3.4 Road Extraction
17(1)
1.3.4.1 Recognition of Roads and of Road Networks
17(2)
1.3.4.2 Benefit of Multi-aspect SAR Images for Road Network Extraction
19(1)
1.3.5 Detection of Individual Buildings
20(1)
1.3.6 SAR Polarimetry
20(1)
1.3.6.1 Basics
21(2)
1.3.6.2 SAR Polarimetry for Urban Analysis
23(1)
1.3.7 Fusion of SAR Images with Complementing Data
24(1)
1.3.7.1 Image Registration
24(1)
1.3.7.2 Fusion for Land Cover Classification
25(1)
1.3.7.3 Feature-Based Fusion of High-Resolution Data
26(1)
1.4 3d Approaches
26(12)
1.4.1 Radargrammetry
27(1)
1.4.1.1 Single Image
27(1)
1.4.1.2 Stereo
28(1)
1.4.1.3 Image Fusion
29(1)
1.4.2 SAR Interferometry
29(1)
1.4.2.1 InSAR Principle
29(3)
1.4.2.2 Analysis of a Single SAR Interferogram
32(2)
1.4.2.3 Multi-image SAR Interferometry
34(1)
1.4.2.4 Multi-aspect InSAR
34(2)
1.4.3 Fusion of InSAR Data and Other Remote Sensing Imagery
36(1)
1.4.4 SAR Polarimetry and Interferometry
37(1)
1.5 Surface Motion
38(2)
1.5.1 Differential SAR Interferometry
38(1)
1.5.2 Persistent Scatterer Interferometry
39(1)
1.6 Moving Object Detection
40(1)
References
41(8)
2 Rapid Mapping Using Airborne and Satellite SAR Images
49(20)
Fabio Dell'Acqua
Paolo Gamba
2.1 Introduction
49(2)
2.2 An Example Procedure
51(6)
2.2.1 Pre-processing of the SAR Images
51(1)
2.2.2 Extraction of Water Bodies
52(1)
2.2.3 Extraction of Human Settlements
53(1)
2.2.4 Extraction of the Road Network
54(2)
2.2.5 Extraction of Vegetated Areas
56(1)
2.2.6 Other Scene Elements
57(1)
2.3 Examples on Real Data
57(7)
2.3.1 The Chengdu Case
58(3)
2.3.2 The Luojiang Case
61(3)
2.4 Conclusions
64(2)
References
66(3)
3 Feature Fusion Based on Bayesian Network Theory for Automatic Road Extraction
69(18)
Uwe Stilla
Karin Hedman
3.1 Introduction
69(1)
3.2 Bayesian Network Theory
70(2)
3.3 Structure of a Bayesian Network
72(9)
3.3.1 Estimating Continuous Conditional Probability Density Functions
76(3)
3.3.2 Discrete Conditional Probabilities
79(1)
3.3.3 Estimating the A-Priori Term
80(1)
3.4 Experiments
81(1)
3.5 Discussion and Conclusion
82(3)
References
85(2)
4 Traffic Data Collection with TerraSAR-X and Performance Evaluation
87(22)
Stefan Hinz
Steffen Suchandt
Diana Weihing
Franz Kurz
4.1 Motivation
87(1)
4.2 SAR Imaging of Stationary and Moving Objects
88(5)
4.3 Detection of Moving Vehicles
93(5)
4.3.1 Detection Scheme
94(2)
4.3.2 Integration of Multi-temporal Data
96(2)
4.4 Matching Moving Vehicles in SAR and Optical Data
98(3)
4.4.1 Matching Static Scenes
98(2)
4.4.2 Temporal Matching
100(1)
4.5 Assessment
101(6)
4.5.1 Accuracy of Reference Data
101(2)
4.5.2 Accuracy of Vehicle Measurements in SAR Images
103(1)
4.5.3 Results of Traffic Data Collection with TerraSAR-X
103(4)
4.6 Summary and Conclusion
107(1)
References
107(2)
5 Object Recognition from Polarimetric SAR Images
109(24)
Ronny Hansch
Olaf Hellwich
5.1 Introduction
109(2)
5.2 SAR Polarimetry
111(6)
5.3 Features and Operators
117(7)
5.4 Object Recognition in PolSAR Data
124(5)
5.5 Concluding Remarks
129(1)
References
130(3)
6 Fusion of Optical and SAR Images
133(28)
Florence Tupin
6.1 Introduction
133(2)
6.2 Comparison of Optical and SAR Sensors
135(3)
6.2.1 Statistics
136(1)
6.2.2 Geometrical Distortions
137(1)
6.3 SAR and Optical Data Registration
138(6)
6.3.1 Knowledge of the Sensor Parameters
138(2)
6.3.2 Automatic Registration
140(1)
6.3.3 A Framework for SAR and Optical Data Registration in Case of HR Urban Images
141(1)
6.3.3.1 Rigid Deformation Computation and Fourier-Mellin Invariant
141(2)
6.3.3.2 Polynomial Deformation
143(1)
6.3.3.3 Results
144(1)
6.4 Fusion of SAR and Optical Data for Classification
144(7)
6.4.1 State of the Art of Optical/SAR Fusion Methods
144(3)
6.4.2 A Framework for Building Detection Based on the Fusion of Optical and SAR Features
147(1)
6.4.2.1 Method Principle
147(1)
6.4.2.2 Best Rectangular Shape Detection
148(1)
6.4.2.3 Complex Shape Detection
149(1)
6.4.2.4 Results
150(1)
6.5 Joint Use of SAR Interferometry and Optical Data for 3D Reconstruction
151(6)
6.5.1 Methodology
151(3)
6.5.2 Extension to the Pixel Level
154(3)
6.6 Conclusion
157(1)
References
157(4)
7 Estimation of Urban DSM from Mono-aspect InSAR Images
161(26)
Celine Tison
Florence Tupin
7.1 Introduction
161(2)
7.2 Review of Existing Methods for Urban DSM Estimation
163(3)
7.2.1 Shape from Shadow
164(1)
7.2.2 Approximation of Roofs by Planar Surfaces
164(1)
7.2.3 Stochastic Geometry
165(1)
7.2.4 Height Estimation Based on Prior Segmentation
165(1)
7.3 Image Quality Requirements for Accurate DSM Estimation
166(3)
7.3.1 Spatial Resolution
166(2)
7.3.2 Radiometric Resolution
168(1)
7.4 DSM Estimation Based on a Markovian Framework
169(14)
7.4.1 Available Data
169(1)
7.4.2 Global Strategy
169(2)
7.4.3 First Level Features
171(1)
7.4.4 Fusion Method: Joint Optimization of Class and Height
172(1)
7.4.4.1 Definition of the Region Graph
172(1)
7.4.4.2 Fusion Model: Maximum A Posteriori Model
173(5)
7.4.4.3 Optimization Algorithm
178(1)
7.4.4.4 Results
178(1)
7.4.5 Improvement Method
179(2)
7.4.6 Evaluation
181(2)
7.5 Conclusion
183(1)
References
184(3)
8 Building Reconstruction from Multi-aspect InSAR Data
187(28)
Antje Thiele
Jan Dirk Wegner
Uwe Soergel
8.1 Introduction
187(1)
8.2 State-of-the-Art
188(2)
8.2.1 Building Reconstruction Through Shadow Analysis from Multi-aspect SAR Data
188(1)
8.2.2 Building Reconstruction from Multi-aspect Polarimetric SAR Data
189(1)
8.2.3 Building Reconstruction from Multi-aspect InSAR Data
189(1)
8.2.4 Iterative Building Reconstruction Using Multi-aspect InSAR Data
190(1)
8.3 Signature of Buildings in High-Resolution InSAR Data
190(7)
8.3.1 Magnitude Signature of Buildings
191(3)
8.3.2 Interferometric Phase Signature of Buildings
194(3)
8.4 Building Reconstruction Approach
197(14)
8.4.1 Approach Overview
197(2)
8.4.2 Extraction of Building Features
199(1)
8.4.2.1 Segmentation of Primitives
199(1)
8.4.2.2 Extraction of Building Parameters
200(1)
8.4.2.3 Filtering of Primitive Objects
201(1)
8.4.2.4 Projection and Fusion of Primitives
202(1)
8.4.3 Generation of Building Hypotheses
202(1)
8.4.3.1 Building Footprint
203(2)
8.4.3.2 Building Height
205(1)
8.4.4 Post-processing of Building Hypotheses
206(1)
8.4.4.1 Ambiguity of the Gable-Roofed Building Reconstruction
206(3)
8.4.4.2 Correction of Oversized Footprints
209(2)
8.5 Results
211(1)
8.6 Conclusion
212(1)
References
213(2)
9 SAR Simulation of Urban Areas: Techniques and Applications
215(18)
Timo Balz
9.1 Introduction
215(1)
9.2 Synthetic Aperture Radar Simulation Development and Classification
216(3)
9.2.1 Development of the SAR Simulation
216(1)
9.2.2 Classification of SAR Simulators
217(2)
9.3 Techniques of SAR Simulation
219(3)
9.3.1 Ray Tracing
219(1)
9.3.2 Rasterization
219(1)
9.3.3 Physical Models Used in Simulations
220(2)
9.4 3D Models as Input Data for SAR Simulations
222(1)
9.4.1 3D Models for SAR Simulation
222(1)
9.4.2 Numerical and Geometrical Problems Concerning the 3D Models
222(1)
9.5 Applications of SAR Simulations in Urban Areas
223(5)
9.5.1 Analysis of the Complex Radar Backscattering of Buildings
223(2)
9.5.2 SAR Data Acquisition Planning
225(1)
9.5.3 SAR Image Geo-referencing
225(1)
9.5.4 Training and Education
226(2)
9.6 Conclusions
228(1)
References
229(4)
10 Urban Applications of Persistent Scatterer Interferometry
233(16)
Michele Crosetto
Oriol Monserrat
Gerardo Herrera
10.1 Introduction
233(4)
10.2 PSI Advantages and Open Technical Issues
237(3)
10.3 Urban Application Review
240(3)
10.4 PSI Urban Applications: Validation Review
243(2)
10.4.1 Results from a Major Validation Experiment
243(1)
10.4.2 PSI Validation Results
244(1)
10.5 Conclusions
245(1)
References
246(3)
11 Airborne Remote Sensing at Millimeter Wave Frequencies
249(24)
Helmut Essen
11.1 Introduction
249(1)
11.2 Boundary Conditions for Millimeter Wave SAR
250(3)
11.2.1 Environmental Preconditions
250(1)
11.2.1.1 Transmission Through the Clear Atmosphere
250(1)
11.2.1.2 Attenuation Due to Rain
250(1)
11.2.1.3 Propagation Through Snow, Fog, Haze and Clouds
250(1)
11.2.1.4 Propagation Through Sand, Dust and Smoke
251(1)
11.2.2 Advantages of Millimeter Wave Signal Processing
251(1)
11.2.2.1 Roughness Related Advantages
251(1)
11.2.2.2 Imaging Errors for Millimeter Wave SAR
252(1)
11.3 The MEMPHIS Radar
253(4)
11.3.1 The Radar System
253(3)
11.3.2 SAR-System Configuration and Geometry
256(1)
11.4 Millimeter Wave SAR Processing for MEMPHIS Data
257(13)
11.4.1 Radial Focussing
257(1)
11.4.2 Lateral Focussing
258(1)
11.4.3 Imaging Errors
259(3)
11.4.4 Millimeter Wave Polarimetry
262(2)
11.4.5 Multiple Baseline Interferometry with MEMPHIS
264(2)
11.4.6 Test Scenarios
266(2)
11.4.7 Comparison of InSAR with LIDAR
268(2)
References
270(3)
Index 273