Understanding Synthetic Aperture Radar Images

Includes CD-ROM
by Chris Oliver
and Shaun Quegan

Features:

• SAR filtering, parameter estimation, image reconstruction, segmentation, and classification.
• Evaluating measurement algorithms to help make better algorithm selections.
• Applying powerful speckle removal methods to airborne and spaceborne SAR data

Written for SAR system designers and remote sensing specialists, this practical new reference shows you how to produce higher quality SAR images using data-driven algorithms, and how to apply powerful new techniques to measure and analyze SAR image content.

Understanding Synthetic Aperture Radar Images describes how SAR imagery is formed, how SAR processing affects image properties, and gives you specific guidance in selecting and applying today’s most sophisticated analytical techniques. By helping you to quickly assess which components of an SAR image should be measured, the book enables you to devote more time and energy to the real task of image interpretation and analysis.

Contents

1. Introduction

2. Principles of SAR Image Formation
   • SAR System Parameters
   • The Range Dimension - Processing a Single Pulse
   • Linear FM (Chirp) Pulses and Pulse Compression
   • The Azimuth Dimension
   • SAR as a Linear Measurement System
   • Calibration

3. Image Defects and Their Correction
   • Defocus
   • Geometric and Radiometric Distortion
   • Residual SAR Imaging Errors
   • Refinements to Signal Based MOCO

4. Fundamental Properties of SAR Images
   • The Nature of Information in SAR Images
   • Single-Channel Image Types and Speckle
   • Estimating the RCS: Multilook Data
   • The Multiplicative Noise Model for Speckle
   • Estimating the RCS - The Effects of Imaging and Noise
   • Consequences of the Imaging Model for SAR Measurements
   • The Effects of Spatial Correlation on Multi-looking
   • Compensating for System-Induced Spatial Correlation
   • Limitation in the Speckle Model
   • Multidimensional SAR Images

5. Data Models
   • Data Characterization
   • Empirical Data Distributions
   • The Product Model
   • Comparison of Distributions
   • Imaging RCS Fluctuations with Finite Resolution
   • Limitations of the Data Model
   • Simulation

6. RCS Reconstruction Filters
   • The Speckle Model and Quality Measures
   • Bayesian Reconstruction
   • Reconstruction using the Speckle Model
   • Reconstruction using the RCS Model
   • Correlated Neighborhood Model
   • Structural Adaptive Filtering
   • Reconstruction using Nonlinear Iterated Processing
   • Reconstructing by Iterating the Structured Neighborhood Model
   • Comparison of Reconstruction Algorithms

7. RCS Classification and Segmentation
   • RCS Classification
   • The Cartoon Model and Segmentation
   • Comparison of Segmentation Algorithms
   • RCS Exploitation

8. Texture Exploitation
   • Model-Free Texture Exploitation
   • Model-Based Texture Parameter Exploitation
   • Texture Classification
   • Texture Segmentation

9. Correlated Textures
   • Model-Based Parameter Estimation
   • Texture Classification
   • ML Correlated Texture Edge Detection

10. Target Information
    • Introduction to Target Detection
    • Constant Target and Background RCS
    • Effect of Background and Target Statistics
    • Further Refinements to Target Detection
    • Introduction to Target Recognition
    • Target Discrimination
    • Target Classification
    • Target Super-Resolution

11. Information in Multichannel SAR Data
    • Polarimetric Measurements
    • Polarimetric Data Distribution
    • The Multidimensional Gaussian Distribution
    • Single-Look Distribution Involving Two Polarimetric Channels
    • Properties of the Distributions
    • Estimation of Polarimetric Parameters
    • The Stokes Scattering Operator Formulation of Polarized Scattering
    • Texture in Polarimetric Data
    • Interferometric, Multifrequency, and Multitemporal Data

12. Analysis Techniques for Multidimensional SAR Images
    • Polarimetric Reconstruction Filters
    • Speckle Reduction by Polarimetric or Multichannel Filtering
    • Measuring and Detecting Differences Between Two Images
    • Multichannel Segmentation
    • Comparison of Algorithms - A Case Study

13. Classification of SAR Imagery
    • The Empirical Basis for Classification
    • Decision Rules for Classification
    • Data-Driven Methods of Classification
    • Classification Based on Scattering Mechanisms
    • Knowledge-Based Classification
    • Using SAR Properties to Refine a Classification

14. Current Status and Future Prospects
    • Are the Models Complete?
    • Are the Models Accurate?
    • Are the Models Useful?
    • Future Developments

Index

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