Hyperspectral Data Processing: Algorithm Design and Analysis

Contents

Cover

Title Page

Copyright

Dedication

Preface

Chapter 1: Overview and Introduction

1.1 Overview

1.2 Issues of Multispectral and Hyperspectral Imageries

1.3 Divergence of Hyperspectral Imagery from Multispectral Imagery

1.4 Scope of This Book

1.5 Book's Organization

1.6 Laboratory Data to be Used in This Book

1.7 Real Hyperspectral Images to be Used in this Book

1.8 Notations and Terminologies to be Used in this Book

I: Preliminaries

Chapter 2: Fundamentals of Subsample and Mixed Sample Analyses

2.1 Introduction

2.2 Subsample Analysis

2.3 Mixed Sample Analysis

2.4 Kernel-Based Classification

2.5 Conclusions

Chapter 3: Three-Dimensional Receiver Operating Characteristics (3D ROC) Analysis

3.1 Introduction

3.2 Neyman–Pearson Detection Problem Formulation

3.3 ROC Analysis

3.4 3D ROC Analysis

3.5 Real Data-Based ROC Analysis

3.6 Examples

3.7 Conclusions

Chapter 4: Design of Synthetic Image Experiments

4.1 Introduction

4.2 Simulation of Targets of Interest

4.3 Six Scenarios of Synthetic Images

4.4 Applications

4.5 Conclusions

Chapter 5: Virtual Dimensionality of Hyperspectral Data

5.1 Introduction

5.2 Reinterpretation of VD

5.3 VD Determined by Data Characterization-Driven Criteria

5.4 VD Determined by Data Representation-Driven Criteria

5.5 Synthetic Image Experiments

5.6 VD Estimated for Real Hyperspectral Images

5.7 Conclusions

Chapter 6: Data Dimensionality Reduction