SAR Image Analysis - A Computational Statistics Approach: With R Code, Data, and Applications

✍ Scribed by Alejandro C. Frery, Jie Wu, Luis Gomez

• Publisher: Wiley-IEEE Press
• Year: 2022
• Tongue: English
• Leaves: 211
• Edition: 1
• Category: Library

✦ Synopsis

SAR IMAGE ANALYSIS — A COMPUTATIONAL STATISTICS APPROACH

Discover how to use statistics to extract information from SAR imagery

In SAR Image Analysis — A Computational Statistics Approach, an accomplished team of researchers delivers a practical exploration of how to use statistics to extract information from SAR imagery. The authors discuss various models, supply sample data and code, and explain theoretical aspects of SAR image analysis that are highly relevant to practitioners and students.

The book offers the theoretical properties of models, estimators, interpretation, data visualization, and advanced techniques, along with the data and code samples, that students require to learn effectively and efficiently.

SAR Image Analysis — A Computational Statistics Approach provides various exercises throughout the book to help readers reinforce and retain the extensive information on parameter estimation, applications, reproducibility, replicability, and advanced topics, like robust estimators and stochastic distances, contained within.

The book also includes:

• Thorough introductions to data acquisition and the elements of data analysis and image processing with R, including useful R packages, preprocessing SAR data, and visualization
• Comprehensive explorations of intensity SAR data and the multiplicative model, including the (SAR) gamma distribution, the K distribution, the G⁰ distribution, and more general distributions under the multiplicative model
• Practical discussions of parameter estimations, including the Bernoulli distribution, the negative binomial distribution, and the uniform distribution
• In-depth examinations of applications, including statistical filters and classification

Perfect for undergraduate and graduate students studying remote sensing, data analysis, and statistics, SAR Image Analysis — A Computational Statistics Approach is also an indispensable resource for researchers, practitioners, and professionals seeking a one-stop resource on how to use statistics to extract information from SAR imagery.

✦ Table of Contents

Cover
Title Page
Copyright
Contents
Foreword by Luis Alvarez
Foreword by Nelson D. A. Mascarenhas
Foreword by Paolo Gamba
Foreword by Xiangrong Zhang
Preface
Acknowledgments
Acronyms
Introduction
About the Companion Website
Chapter 1 Data Acquisition
1.1 Introduction
1.2 SAR
1.2.1 The Radar
1.2.2 What is SAR?
1.2.3 SAR Systems
1.2.4 The Synthetic Antenna
1.3 Spatial Resolution
1.4 SAR Imaging Techniques
1.5 The Return Signal: Backscatter and Speckle
1.5.1 Backscatter
1.5.2 Speckle
1.5.3 SAR Geometric Distortions
1.6 SAR Satellites
1.6.1 European Mission: Sentinel‐1
1.6.2 European Mission: COSMO‐SkyMed Systems
1.6.3 European Mission: TerraSAR‐X
1.6.4 Canadian and NASA Missions
1.6.5 Japanesse Mission
1.6.6 Chinese Mission
1.7 Copernicus Open Access Hub
1.8 NASA Earth Data Open Data
1.9 Actual SAR Data Examples
1.9.1 Hawaii's Big Island
1.9.2 Other Examples
Exercises
Chapter 2 Elements of Data Analysis and Image Processing with R
2.1 Useful R Packages
2.1.1 Data Loading
2.1.2 Data Manipulation
2.2 Descriptive Statistics
2.2.1 Center Tendency of Data
2.2.2 Dispersion of Data
2.2.3 Shape of Data
2.3 Visualization
2.3.1 Rug and Box Plots
2.3.2 Histogram
2.3.3 Scattering Diagram
2.4 Statistics and Image Processing
2.4.1 Histogram‐Based Image Transformation
2.4.2 Scattering based Analysis
2.5 The imagematrix Package
Chapter 3 Intensity SAR Data and the Multiplicative Model
3.1 The K Distribution
3.2 The G0 Distribution
3.3 The 𝒢H Distribution
3.4 Connection Between Models
Exercises
Chapter 4 Parameter Estimation
4.1 Models
4.1.1 The Bernoulli Distribution
4.1.2 The Binomial Distribution
4.1.3 The Negative Binomial Distribution
4.1.4 The Uniform Distribution
4.1.5 Beta Distribution
4.1.6 The Gaussian Distribution
4.1.7 Mixture of Gaussian Distributions
4.1.8 The (SAR) Gamma Distribution
4.1.9 The Reciprocal Gamma Distribution
4.1.10 The 𝒢I0 Distribution
4.2 Inference by Analogy
4.2.1 The Uniform Distribution
4.2.2 The Gaussian Distribution
4.2.3 Mixture of Gaussian Distributions
4.2.4 The (SAR) Gamma Distribution
4.3 Inference by Maximum Likelihood
4.3.1 The Uniform Distribution
4.3.2 The Gaussian Distribution
4.3.3 Mixture of Gaussian Distributions
4.3.4 The (SAR) Gamma Distribution
4.3.5 The 𝒢0 Distribution
4.4 Analogy vs. Maximum Likelihood
4.5 Improvement by Bootstrap
4.6 Comparison of Estimators
4.7 An Example
4.8 The Same Example, Revisited
4.9 Another Example
Exercises
Chapter 5 Applications
5.1 Statistical Filters: Mean, Median, Lee
5.1.1 Mean Filter
5.1.2 Median Filter
5.1.3 Lee Filter
5.2 Advanced Filters: MAP and Nonlocal Means
5.2.1 MAP Filters
5.2.2 Nonlocal Means Filter
5.2.3 Statistical NLM Filters
5.2.3.1 Transforming p‐Values into Weights
5.2.4 The Statistical Test
5.3 Implementation Details
5.4 Results
5.5 Classification
5.5.1 The Image Space of the SAR Data
5.5.2 The Feature Space
5.5.3 Similarity Criterion
5.6 Supervised Image Classification of SAR Data
5.6.1 The Nearest Neighbor Classifier
5.6.2 The K‐nn Method
5.7 Maximum Likelihood Classifier
5.8 Unsupervised Image Classification of SAR Data: The K‐means Classifier
5.9 Assessment of Classification Results
Exercises
Chapter 6 Advanced Topics
6.1 Assessment of Despeckling Filters
6.2 Standard Metrics
6.2.1 Advanced Metrics for SAR Despeckling Assessment
6.2.2 Completing the Assessment
6.3 Robustness
6.3.1 Robust Inference
6.3.2 The Mean and the Median
6.3.3 Empirical Stylized Influence Function
6.4 Rejoinder and Recommendations
Chapter 7 Reproducibility and Replicability
7.1 What Is Reproducibility?
7.2 What Is Replicability?
7.3 Reproducibility and Replicability: Benefits for the Remote Sensing Community
7.4 Recommendations for Making “Good Science”
7.5 Conclusions
Bibliography
Index
EULA