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 authors first book Hyperspectral Imaging: Techniques for Spectral Detection and Classification without much overlap ... 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. 19 Exploitation based hyperspectral data compression 20 Progressive spectral dimensionality process 21 Progressive band dimentsionality process 22 Dynamic dimensionality allocation 23 Progressive band selection 24 Binary coding for spectral signatures 25 Vector coding for hyperspectral signatures 26 Progressive coding for spectral signatures 27 Variable number variable band selection for hyperspectral signals 28 Kalman filter based estimation for hyperspectral signals 29 Wavelet representation for hyperspectral signals 30 Application fof target detection 31 Nonlinear dimensionality expansion to multispectral imagery 32 Multispectral magetics resonance imaging 33 Conclusions. 1 Overview and introduction 2 Fundamentals of subsample and mixed sample analyses 3 Three dimentsional receiver operating characteristics (3D ROC) analysis 4 Design of synthetic image experiments 5 Virtual dimensionality of hyperspectral data 6 Data dimensionality reduction 7 Simultaneous endmember extraction algorithms (SM EEAs) 8 Sequential endmember extraction algorithms (SQ EEAs) 9 Initialization driven endmember extraction algorithms (ID EEAs) 10 Random endmember extraction algorithms (REEAs) 11 Exploration on relationships among endmember extraction algorithms 12 Orthogonal subspace projection revisited 13 Fishers linear spectral mixture analysis 14 Weighted abundance constrained linear spectral mixture analysis 15 Kernel based linear spectral mixture analysis 16 Hyperspectral measures 17 Unsupervised linear hyperspectral mixture analyis 18 Pixel extraction and information.