Hyperspectral Data Processing : Algorithm Design and Analysis

__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. Content: Chapter 1 Overview and Introduction (pages 1–30): Chapter 2 Fundamentals of Subsample and Mixed Sample Analyses (pages 33–62): Chapter 3 Three?Dimensional Receiver Operating Characteristics (3D ROC) Analysis (pages 63–100): Chapter 4 Design of Synthetic Image Experiments (pages 101–123): Chapter 5 Virtual Dimensionality of Hyperspectral Data (pages 124–167): Chapter 6 Data Dimensionality Reduction (pages 168–199): Chapter 7 Simultaneous Endmember Extraction Algorithms (SM?EEAs) (pages 207–240): Chapter 8 Sequential Endmember Extraction Algorithms (SQ?EEAs) (pages 241–264): Chapter 9 Initialization?Driven Endmember Extraction Algorithms (ID?EEAs) (pages 265–286): Chapter 10 Random Endmember Extraction Algorithms (REEAs) (pages 287–315): Chapter 11 Exploration on Relationships among Endmember Extraction Algorithms (pages 316–349): Chapter 12 Orthogonal Subspace Projection Revisited (pages 355–390): Chapter 13 Fisher's Linear Spectral Mixture Analysis (pages 391–410): Chapter 14 Weighted Abundance?Constrained Linear Spectral Mixture Analysis (pages 411–433): Chapter 15 Kernel?Based Linear Spectral Mixture Analysis (pages 434–463): Chapter 16 Hyperspectral Measures (pages 469–482): Chapter 17 Unsupervised Linear Hyperspectral Mixture Analysis (pages 483–525): Chapter 18 Pixel Extraction and Information (pages 526–540): Chapter 19 Exploitation?Based Hyperspectral Data Compression (pages 545–580): Chapter 20 Progressive Spectral Dimensionality Process (pages 581–612): Chapter 21 Progressive Band Dimensionality Process (pages 613–663): Chapter 22 Dynamic Dimensionality Allocation (pages 664–682): Chapter 23 Progressive Band Selection (pages 683–715): Chapter 24 Binary Coding for Spectral Signatures (pages 719–740): Chapter 25 Vector Coding for Hyperspectral Signatures (pages 741–771): Chapter 26 Progressive Coding for Spectral Signatures (pages 772–796): Chapter 27 Variable?Number Variable?Band Selection for Hyperspectral Signals (pages 799–819): Chapter 28 Kalman Filter?Based Estimation for Hyperspectral Signals (pages 820–858): Chapter 29 Wavelet Representation for Hyperspectral Signals (pages 859–875): Chapter 30 Applications of Target Detection (pages 879–896): Chapter 31 Nonlinear Dimensionality Expansion to Multispectral Imagery (pages 897–919): Chapter 32 Multispectral Magnetic Resonance Imaging (pages 920–955): Chapter 33 Conclusions (pages 956–991):

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.

"This book is intended to be a sequel from the author's other title with Kluwer "Hyperspectral Imaging: Techniques for Spectral Detection and Classification". It contains five major parts. Part I is new aspects of OSP including 7 chapters, OSP revisit, generalized OSP, FPGA designs for OSP and CEM, Kalman filter-based linear unmixing, least squares fully constrained linear mixture analysis, exploitation-based hyperspectral data compression and size estimation of supixel targets, Part II is interference rejection for linear unmixing composed of three chapters, signal-composed interference-annihilated theory, interference-annihilated noise-adjusted theory and information-processed matched filter theory; Part III is nonlinear non-literal techniques for linear unmixing consisting of 3 chapters, convex cone analysis, information theoretic criterion-based project pursuit and nonlinear mixing model analysis; Part IV is spectral coding comprising of three chapters, progressive spectral coding, spectral binary coding and spectral coding for band selection; Part V is applications made up of two chapters, applications to magnetic resonance imaging and landmine detection"-- Provided by publisher