Preface; Contents; About the Author; Chapter 1: Introduction; 1.1 Introduction; 1.2 Recursive Hyperspectral Sample Processing ; 1.2.1 Sample Spectral Statistics-Based Recursive Hyperspectral Sample Processing; 1.2.2 Signature Spectral Statistics-Based Recursive Hyperspectral Sample Processing; 1.3 Recursive Hyperspectral Band Processing ; 1.3.1 Band Selection ; 1.3.2 Progressive Hyperspectral Band Processing ; 1.3.3 Recursive Hyperspectral Band Processing ; 1.3.3.1 Sample Spectral Statistics-Based Recursive Hyperspectral Band Processing

1.3.3.2 Sample Spectral Statistics-Based Recursive Hyperspectral Band Processing1.4 Scope of Book; 1.4.1 Part I: Fundamentals; 1.4.2 Part II: Sample Spectral Statistics-Based Recursive Hyperspectral Sample Processing ; 1.4.3 Part III: Signature Spectral Statistics-Based Recursive Hyperspectral Sample Processing ; 1.4.4 Part IV: Sample Statistics-Based Recursive Hyperspectral Band Processing ; 1.4.5 Part V: Signature Statistics-Based Recursive Hyperspectral Band Processing ; 1.5 Real Hyperspectral Images to Be Used in This Book; 1.5.1 AVIRIS Data; 1.5.1.1 Cuprite Data

1.5.1.2 Lunar Crater Volcanic Field 1.5.2 HYDICE Data; 1.5.3 Hyperion Data; 1.6 Synthetic Images to Be Used in this Book; 1.7 How to Use this Book; 1.8 Notations and Terminology Used in the Book; Part I: Fundamentals; Chapter 2: Simplex Volume Calculation; 2.1 Introduction; 2.2 Determinant-Based Simplex Volume Calculation; 2.3 Geometric Simplex Volume Calculation; 2.4 General Theorem for Geometric Simplex Volume Calculation; 2.5 A Mathematical Toy Example; 2.6 Real Image Experiments; 2.7 Conclusions; Chapter 3: Discrete-Time Kalman Filtering for Hyperspectral Processing; 3.1 Introduction

3.2 Discrete-Time Kalman Filtering3.2.1 A Priori and A Posteriori State Estimates; 3.2.2 Finding an Optimal Kalman Gain K(k); 3.2.3 Orthogonality Principle; 3.2.4 Discrete-Time Kalman Predictor and Filter; 3.3 Kalman Filter-Based Linear Spectral Mixture Analysis; 3.4 Kalman Filter-Based Hyperspectral Signal Processing; 3.4.1 Kalman Filter-Based Hyperspectral Signal Processing; 3.4.2 Kalman Filter-Based Spectral Signature Estimator ; 3.4.3 Kalman Filter-Based Spectral Signature Identifier ; 3.4.4 Kalman Filter-Based Spectral Signature Quantifier ; 3.5 Conclusions

Chapter 4: Target-Specified Virtual Dimensionality for Hyperspectral Imagery4.1 Introduction; 4.2 Review of VD; 4.3 Eigen-Analysis-Based VD; 4.3.1 Binary Composite Hypothesis Testing Formulation; 4.3.1.1 HFC Method; 4.3.1.2 Maximum Orthogonal Complement Algorithm; 4.3.2 Discussions of HFC Method and MOCA; 4.4 Finding Targets of Interest; 4.4.1 What Are Targets of Interest?; 4.4.2 Second-Order-Statistics (2OS)-Specified Target VD; 4.4.2.1 OSP-Specified Targets; 4.4.2.2 Least-Squares-Specified Targets; Unsupervised Least-Squares OSP Method