Book description
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.
Table of 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
- Chapter 3: Three-Dimensional Receiver Operating Characteristics (3D ROC) Analysis
- Chapter 4: Design of Synthetic Image Experiments
- Chapter 5: Virtual Dimensionality of Hyperspectral Data
-
Chapter 6: Data Dimensionality Reduction
- 6.1 Introduction
- 6.2 Dimensionality Reduction by Second-Order Statistics-Based Component Analysis Transforms
- 6.3 Dimensionality Reduction by High-Order Statistics-Based Components Analysis Transforms
- 6.4 Dimensionality Reduction by Infinite-Order Statistics-Based Components Analysis Transforms
- 6.5 Dimensionality Reduction by Projection Pursuit-Based Components Analysis Transforms
- 6.6 Dimensionality Reduction by Feature Extraction-Based Transforms
- 6.7 Dimensionality Reduction by Band Selection
- 6.8 Constrained Band Selection
- 6.9 Conclusions
-
II: Endmember Extraction
- Chapter 7: Simultaneous Endmember Extraction Algorithms (SM-EEAs)
- Chapter 8: Sequential Endmember Extraction Algorithms (SQ-EEAs)
- Chapter 9: Initialization-Driven Endmember Extraction Algorithms (ID-EEAs)
- Chapter 10: Random Endmember Extraction Algorithms (REEAs)
- Chapter 11: Exploration on Relationships among Endmember Extraction Algorithms
-
III: Supervised Linear Hyperspectral Mixture Analysis
- Chapter 12: Orthogonal Subspace Projection Revisited
-
Chapter 13: Fisher's Linear Spectral Mixture Analysis
- 13.1 Introduction
- 13.2 Feature Vector-Constrained FLSMA (FVC-FLSMA)
- 13.3 Relationship Between FVC-FLSMA and LCMV, TCIMF, and CEM
- 13.4 Relationship Between FVC-FLSMA and OSP
- 13.5 Relationship Between FVC-FLSMA and LCDA
- 13.6 Abundance-Constrained Least Squares FLDA (ACLS-FLDA)
- 13.7 Synthetic Image Experiments
- 13.8 Real Image Experiments
- 13.9 Conclusions
- Chapter 14: Weighted Abundance-Constrained Linear Spectral Mixture Analysis
- Chapter 15: Kernel-Based Linear Spectral Mixture Analysis
- IV: Unsupervised Hyperspectral Image Analysis
- V: Hyperspectral Information Compression
- VI: Hyperspectral Signal Coding
- VII: Hyperspectral Signal Characterization
-
VIII: Applications
- Chapter 30: Applications of Target Detection
- Chapter 31: Nonlinear Dimensionality Expansion to Multispectral Imagery
- Chapter 32: Multispectral Magnetic Resonance Imaging
-
Chapter 33: Conclusions
- 33.1 Design Principles for Nonliteral Hyperspectral Imaging Techniques
- 33.2 Endemember Extraction
- 33.3 Linear Spectral Mixture Analysis
- 33.4 Anomaly Detection
- 33.5 Support Vector Machines and Kernel-Based Approaches
- 33.6 Hyperspectral Compression
- 33.7 Hyperspectral Signal Processing
- 33.8 Applications
- 33.9 Further Topics
- Glossary
- Appendix: Algorithm Compendium
- References
- Index
Product information
- Title: Hyperspectral Data Processing: Algorithm Design and Analysis
- Author(s):
- Release date: April 2013
- Publisher(s): Wiley-Interscience
- ISBN: 9780471690566
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