Radar Remote Sensing of Urban Areas

Preface

Contents

1 Review of Radar Remote Sensing on Urban Areas

1.1 Introduction

1.2 Basics

1.2.1 Imaging Radar

1.2.2 Mapping of 3d Objects

1.3 2d Approaches

1.3.1 Pre-processing and Segmentation of Primitive Objects

1.3.2 Classification of Single Images

1.3.2.1 Detection of Settlements

1.3.2.2 Characterization of Settlements

1.3.3 Classification of Time-Series of Images

1.3.4 Road Extraction

1.3.4.1 Recognition of Roads and of Road Networks

1.3.4.2 Benefit of Multi-aspect SAR Images for Road Network Extraction

1.3.5 Detection of Individual Buildings

1.3.6 SAR Polarimetry

1.3.6.1 Basics

1.3.6.2 SAR Polarimetry for Urban Analysis

1.3.7 Fusion of SAR Images with Complementing Data

1.3.7.1 Image Registration

1.3.7.2 Fusion for Land Cover Classification

1.3.7.3 Feature-Based Fusion of High-Resolution Data

1.4 3d Approaches

1.4.1 Radargrammetry

1.4.1.1 Single Image

1.4.1.2 Stereo

1.4.1.3 Image Fusion

1.4.2 SAR Interferometry

1.4.2.1 InSAR Principle

1.4.2.2 Analysis of a Single SAR Interferogram

1.4.2.3 Multi-image SAR Interferometry

1.4.2.4 Multi-aspect InSAR

1.4.3 Fusion of InSAR Data and Other Remote Sensing Imagery

1.4.4 SAR Polarimetry and Interferometry

1.5 Surface Motion

1.5.1 Differential SAR Interferometry

1.5.2 Persistent Scatterer Interferometry

1.6 Moving Object Detection

References

2 Rapid Mapping Using Airborne and Satellite SAR Images

2.1 Introduction

2.2 An Example Procedure

2.2.1 Pre-processing of the SAR Images

2.2.2 Extraction of Water Bodies

2.2.3 Extraction of Human Settlements

2.2.4 Extraction of the Road Network

2.2.5 Extraction of Vegetated Areas

2.2.6 Other Scene Elements

2.3 Examples on Real Data

2.3.1 The Chengdu Case

2.3.2 The Luojiang Case

2.4 Conclusions

References

3 Feature Fusion Based on Bayesian Network Theory for Automatic Road Extraction

3.1 Introduction

3.2 Bayesian Network Theory

3.3 Structure of a Bayesian Network

3.3.1 Estimating Continuous Conditional Probability Density Functions

3.3.2 Discrete Conditional Probabilities

3.3.3 Estimating the A-Priori Term

3.4 Experiments

3.5 Discussion and Conclusion

References

4 Traffic Data Collection with TerraSAR-Xand Performance Evaluation

4.1 Motivation

4.2 SAR Imaging of Stationary and Moving Objects

4.3 Detection of Moving Vehicles

4.3.1 Detection Scheme

4.3.2 Integration of Multi-temporal Data

4.4 Matching Moving Vehicles in SAR and Optical Data

4.4.1 Matching Static Scenes

4.4.2 Temporal Matching

4.5 Assessment

4.5.1 Accuracy of Reference Data

4.5.2 Accuracy of Vehicle Measurements in SAR Images

4.5.3 Results of Traffic Data Collectionwith TerraSAR-X

4.6 Summary and Conclusion

References

5 Object Recognition from Polarimetric SAR Images

5.1 Introduction

5.2 SAR Polarimetry

5.3 Features and Operators

5.4 Object Recognition in PolSAR Data

5.5 Concluding Remarks

References

6 Fusion of Optical and SAR Images

6.1 Introduction

6.2 Comparison of Optical and SAR Sensors

6.2.1 Statistics

6.2.2 Geometrical Distortions

6.3 SAR and Optical Data Registration

6.3.1 Knowledge of the Sensor Parameters

6.3.2 Automatic Registration

6.3.3 A Framework for SAR and Optical Data Registration in Case of HR Urban Images

6.3.3.1 Rigid Deformation Computation and Fourier--Mellin Invariant

6.3.3.2 Polynomial Deformation

6.3.3.3 Results

6.4 Fusion of SAR and Optical Data for Classification

6.4.1 State of the Art of Optical/SAR Fusion Methods

6.4.2 A Framework for Building Detection Based on the Fusion of Optical and SAR Features

6.4.2.1 Method Principle

6.4.2.2 Best Rectangular Shape Detection

6.4.2.3 Complex Shape Detection

6.4.2.4 Results

6.5 Joint Use of SAR Interferometry and Optical Data for 3D Reconstruction

6.5.1 Methodology

6.5.2 Extension to the Pixel Level

6.6 Conclusion

References

7 Estimation of Urban DSM from Mono-aspect InSAR Images

7.1 Introduction

7.2 Review of Existing Methods for Urban DSM Estimation

7.2.1 Shape from Shadow

7.2.2 Approximation of Roofs by Planar Surfaces

7.2.3 Stochastic Geometry

7.2.4 Height Estimation Based on Prior Segmentation

7.3 Image Quality Requirements for Accurate DSM Estimation

7.3.1 Spatial Resolution

7.3.2 Radiometric Resolution

7.4 DSM Estimation Based on a Markovian Framework

7.4.1 Available Data

7.4.2 Global Strategy

7.4.3 First Level Features

7.4.4 Fusion Method: Joint Optimization of Class and Height

7.4.4.1 Definition of the Region Graph

7.4.4.2 Fusion Model: MaximumA Posteriori Model

7.4.4.3 Optimization Algorithm

7.4.4.4 Results

7.4.5 Improvement Method

7.4.6 Evaluation

7.5 Conclusion

References

8 Building Reconstruction from Multi-aspect InSAR Data

8.1 Introduction

8.2 State-of-the-Art

8.2.1 Building Reconstruction Through Shadow Analysis from Multi-aspect SAR Data

8.2.2 Building Reconstruction from Multi-aspect Polarimetric SAR Data

8.2.3 Building Reconstruction from Multi-aspect InSAR Data

8.2.4 Iterative Building ReconstructionUsing Multi-aspect InSAR Data

8.3 Signature of Buildings in High-Resolution InSAR Data

8.3.1 Magnitude Signature of Buildings

8.3.2 Interferometric Phase Signature of Buildings

8.4 Building Reconstruction Approach

8.4.1 Approach Overview

8.4.2 Extraction of Building Features

8.4.2.1 Segmentation of Primitives

8.4.2.2 Extraction of Building Parameters

8.4.2.3 Filtering of Primitive Objects

8.4.2.4 Projection and Fusion of Primitives

8.4.3 Generation of Building Hypotheses

8.4.3.1 Building Footprint

8.4.3.2 Building Height

8.4.4 Post-processing of Building Hypotheses

8.4.4.1 Ambiguity of the Gable-Roofed Building Reconstruction

8.4.4.2 Correction of Oversized Footprints

8.5 Results

8.6 Conclusion

References

9 SAR Simulation of Urban Areas: Techniques and Applications

9.1 Introduction

9.2 Synthetic Aperture Radar Simulation Development and Classification

9.2.1 Development of the SAR Simulation

9.2.2 Classification of SAR Simulators

9.3 Techniques of SAR Simulation

9.3.1 Ray Tracing

9.3.2 Rasterization

9.3.3 Physical Models Used in Simulations

9.4 3D Models as Input Data for SAR Simulations

9.4.1 3D Models for SAR Simulation

9.4.2 Numerical and Geometrical Problems Concerning the 3D Models

9.5 Applications of SAR Simulations in Urban Areas

9.5.1 Analysis of the Complex Radar Backscattering of Buildings

9.5.2 SAR Data Acquisition Planning

9.5.3 SAR Image Geo-referencing

9.5.4 Training and Education

9.6 Conclusions

References

10 Urban Applications of Persistent Scatterer Interferometry

10.1 Introduction

10.2 PSI Advantages and Open Technical Issues

10.3 Urban Application Review

10.4 PSI Urban Applications: Validation Review

10.4.1 Results from a Major Validation Experiment

10.4.2 PSI Validation Results

10.5 Conclusions

References

11 Airborne Remote Sensing at Millimeter Wave Frequencies

11.1 Introduction

11.2 Boundary Conditions for Millimeter Wave SAR

11.2.1 Environmental Preconditions

11.2.1.1 Transmission Through the Clear Atmosphere

11.2.1.2 Attenuation Due to Rain

11.2.1.3 Propagation Through Snow, Fog, Haze and Clouds

11.2.1.4 Propagation Through Sand, Dust and Smoke

11.2.2 Advantages of Millimeter Wave Signal Processing

11.2.2.1 Roughness Related Advantages

11.2.2.2 Imaging Errors for Millimeter Wave SAR

11.3 The MEMPHIS Radar

11.3.1 The Radar System

11.3.2 SAR-System Configuration and Geometry

11.4 Millimeter Wave SAR Processing for MEMPHIS Data

11.4.1 Radial Focussing

11.4.2 Lateral Focussing

11.4.3 Imaging Errors

11.4.4 Millimeter Wave Polarimetry

11.4.5 Multiple Baseline Interferometry with MEMPHIS

11.4.6 Test Scenarios

11.4.7 Comparison of InSAR with LIDAR

References

Index