Hyperspectral Data Processing

Hyperspectral Data Processing: Algorithm Design and
Analysis is a culmination of the research conducted in the
Remote Sensing Signal and Image Processing Laboratory (RSSIPL) at
the University of Maryland, Baltimore County. Specifically, it
treats hyperspectral image processing and hyperspectral signal
processing as separate subjects in two different categories. Most
materials covered in this book can be used in conjunction with the
author's first book, Hyperspectral Imaging: Techniques for
Spectral Detection and Classification, without much
overlap.

Many results in this book are either new or have not been
explored, presented, or published in the public domain. These
include various aspects of endmember extraction, unsupervised
linear spectral mixture analysis, hyperspectral information
compression, hyperspectral signal coding and characterization, as
well as applications to conceal target detection, multispectral
imaging, and magnetic resonance imaging. Hyperspectral Data
Processing contains eight major sections:

• Part I: provides fundamentals of hyperspectral data
  processing

• Part II: offers various algorithm designs for endmember
  extraction

• Part III: derives theory for supervised linear spectral mixture
  analysis

• Part IV: designs unsupervised methods for hyperspectral image
  analysis

• Part V: explores new concepts on hyperspectral information
  compression

• Parts VI & VII: develops techniques for hyperspectral
  signal coding and characterization

• Part VIII: presents applications in multispectral imaging and
  magnetic resonance imaging

Hyperspectral Data Processing compiles an algorithm
compendium with MATLAB codes in an appendix to help readers
implement many important algorithms developed in this book and
write their own program codes without relying on software
packages.

Hyperspectral Data Processing is a valuable reference for
those who have been involved with hyperspectral imaging and its
techniques, as well those who are new to the subject.

Auteur

CHEIN-I CHANG, PhD, is a Professor in the Department of
Computer Science and Electrical Engineering at the University of
Maryland, Baltimore County. He established the Remote Sensing
Signal and Image Processing Laboratory and conducts research in
designing and developing signal processing algorithms for
hyperspectral imaging, medical imaging, and documentation analysis.
A Fellow of IEEE and SPIE, Dr. Chang has published over 125
refereed journal articles, including more than forty papers in the
IEEE Transaction on Geoscience and Remote Sensing. In
addition to authoring Hyperspectral Imaging: Techniques for
Spectral Detection and Classification, as well as editing two
books, Hyperspectral Data Exploitation: Theory and
Applications and Recent Advances in Hyperspectral Signal and
Imaging Processing and co-editing one book, High Performance
Computing in Remote Sensing, he holds five patents and has
several pending.

Résumé

Hyperspectral Data Processing: Algorithm Design and Analysis is a culmination of the research conducted in the Remote Sensing Signal and Image Processing Laboratory (RSSIPL) at the University of Maryland, Baltimore County. Specifically, it treats hyperspectral image processing and hyperspectral signal processing as separate subjects in two different categories. Most materials covered in this book can be used in conjunction with the author's first book, Hyperspectral Imaging: Techniques for Spectral Detection and Classification, without much overlap.

Many results in this book are either new or have not been explored, presented, or published in the public domain. These include various aspects of endmember extraction, unsupervised linear spectral mixture analysis, hyperspectral information compression, hyperspectral signal coding and characterization, as well as applications to conceal target detection, multispectral imaging, and magnetic resonance imaging. Hyperspectral Data Processing contains eight major sections:

• Part I: provides fundamentals of hyperspectral data processing
• Part II: offers various algorithm designs for endmember extraction
• Part III: derives theory for supervised linear spectral mixture analysis
• Part IV: designs unsupervised methods for hyperspectral image analysis
• Part V: explores new concepts on hyperspectral information compression
• Parts VI & VII: develops techniques for hyperspectral signal coding and characterization
• Part VIII: presents applications in multispectral imaging and magnetic resonance imaging
  Hyperspectral Data Processing compiles an algorithm compendium with MATLAB codes in an appendix to help readers implement many important algorithms developed in this book and write their own program codes without relying on software packages.

Hyperspectral Data Processing is a valuable reference for those who have been involved with hyperspectral imaging and its techniques, as well those who are new to the subject.

Contenu
PREFACE xxiii 1 OVERVIEWAND INTRODUCTION 1

1.1 Overview 2

1.2 Issues of Multispectral and Hyperspectral Imageries 3

1.3 Divergence of Hyperspectral Imagery from Multispectral Imagery 4

1.4 Scope of This Book 7

1.5 Book's Organization 10

1.6 Laboratory Data to be Used in This Book 19

1.7 Real Hyperspectral Images to be Used in this Book 20

1.8 Notations and Terminologies to be Used in this Book 29

I: PRELIMINARIES 31

2 FUNDAMENTALS OF SUBSAMPLE AND MIXED SAMPLE ANALYSES 33

2.1 Introduction 33

2.2 Subsample Analysis 35

2.3 Mixed Sample Analysis 45

2.4 Kernel-Based Classification 57

2.5 Conclusions 60

3 THREE-DIMENSIONAL RECEIVER OPERATING CHARACTERISTICS (3D ROC) ANALYSIS 63

3.1 Introduction 63

3.2 NeymanPearson Detection Problem Formulation 65

3.3 ROC Analysis 67

3.4 3D ROC Analysis 69

3.5 Real Data-Based ROC Analysis 72

3.6 Examples 78

3.7 Conclusions 99

4 DESIGN OF SYNTHETIC IMAGE EXPERIMENTS 101

4.1 Introduction 102

4.2 Simulation of Targets of Interest 103

4.3 Six Scenarios of Synthetic Images 104

4.4 Applications 112

4.5 Conclusions 123

5 VIRTUAL DIMENSIONALITY OF HYPERSPECTRAL DATA 124

5.1 Introduction 124

5.2 Reinterpretation of VD 126

5.3 VD Determined by Data Characterization-Driven Criteria 126

5.4 VD Determined by Data Representation-Driven Criteria 140

5.5 Synthetic Image Experiments 144

5.6 VD Estimated for Real Hyperspectral Images 155

5.7 Conclusions 163

6 DATA DIMENSIONALITY REDUCTION 168

6.1 Introduction 168

6.2 Dimensionality Reduction by Second-Order Statistics-Based Component Analysis Transforms 170

6.3 Dimensionality Reduction by High-Order Statistics-Based Components Analysis Transforms 179

6.4 Dimensionality Reduction by Infinite-Order Statistics-Based Components Analysis Transforms 184

6.5 Dimensionality Reduction by Projection Pursuit-Based Components Analysis Transforms 190

6.6 Dimensionality Reduction by Feature Extraction-Based Transforms 195

6.7 Dimensionality Reduction by Band Selection 196

6.8 Constrained Band Selection 197

6.9 Conclusions 198

II: ENDMEMBER EXTRACTION 201

7 SIMULTANEOUS ENDMEMBER EXTRACTION ALGORITHMS (SM-EEAs) 207

7.1 Introduction 208

7.2 Convex Geometry-Based Endmember Extraction 209

7.3 Second-Order Statistics-Based Endmember Extraction 228

7.4 Automated Morphological Endmember Extraction (AMEE) 230

7.5 Experiments 231

7.6 Conclusions 239

8 SEQUENTIAL ENDMEMBER EXTRACTION ALGORITHMS (SQ-EEAs) 241

8.1 Introduction 241

8.2 Successive N-FINDR (SC N-FINDR) 244

8.3 Simplex Growing Algorithm (SGA) 244

8.4 Vertex Component Analysis (VCA) 247

8.5 Linear Spectral Mixture Analysis-Based SQ-EEAs 248

8.6…