Real-Time Recursive Hyperspectral Sample and Band Processing (eBook)

Chein-I Chang is Professor with Department of Computer Science and Electrical Engineering at the University of Maryland, Baltimore County. He established a 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. Dr. Chang has published over 146 referred journal articles including more than 50 papers in the IEEE Transaction on Geoscience and Remote Sensing alone and four patents with several pending on hyperspectral image processing. He authored two books, Hyperspectral Imaging: Techniques for Spectral Detection and Classification (Kluwer Academic Publishers, 2003) and Hyperspectral Data Processing: Algorithm Design and Analysis (Wiley, 2013). He also edited two books, Recent Advances in Hyperspectral Signal and Image Processing (Transworld Research Network, India, 2006) and Hyperspectral Data Exploitation: Theory and Applications (John Wiley & Sons, 2007) and co-edited with A. Plaza a book on High Performance Computing in Remote Sensing (CRC Press, 2007). Dr. Chang has received his Ph.D. in Electrical Engineering from University of Maryland, College Park. He is a Fellow of IEEE and SPIE with contributions to hyperspectral image processing.

Chapter 1: Overview and IntroductionPART I: Fundamentals Chapter 2:Simplex Volume CalculationChapter 3:Discrete Time Kalman Filtering in Hyperspectral Data PrcoessingChapter 4:Target-Specified Virtual DimesnionalityPART II: Sample Spectral Statistics-Based Recursive Hyperspectral Sample Prcoessing Chapter 5:Real Time Recursive Hyperspectral Sample Processing of Constrained Energy MinimizationChapter 6:Real Time Recursive Hyperspectral Sample Processing of Anomaly DetectionPART III: Signature Spectral Statistics-Based Recursive Hyperspectral Sample Prcoessing Chapter 7: Recursive Hyperspectral Sample Processing of Automatic Target Generation ProcessChapter 8: Recursive Hyperspectral Sample Processing of Orthogonal Subspace ProjectionChapter 9: Recursive Hyperspectral Sample Processing of Linear Spectral Mixture AnalysisChapter 10:Recursive Hyperspectral Sample Processing of Maximimal Likelihood EstimationChapter 11: Recursive Hyperspectral Sample Processing of Orthogonal Projection-Based Simplex Growing AlgorithmChapter 12: Recursive Hyperspectral Sample Processing of Geometric Simplex Growing Simplex AlgorithmPART IV: Sample Spectral Statistics-Based Recursive Hyperspectral Band Prcoessing Chapter 13:Recursive Hyperspectral Band Processing of Constrained Energy Minimization Chapter 14:Recursive Hyperspectral Band Processing of Anomly DetectionPART V: Signature Spectral Statistics-Based Recursive Hyperspectral Band Prcoessing Chapter 15:Recursive Hyperspectral Band Processing of Automatic Target Generation Process Chapter 16:Recursive Hyperspectral Band Processing of Orthogonal Subspce Projection Chapter 17:Recursive Hyperspectral Band Processing of Linear Spectral Mixture Analysis Chapter 18:Recursive Hyperspectral Band Processing of Growing Simplex Volume Analysis Chapter 19:Recursive Hyperspectral Band Processing of Iterative Pixel Puirty Index Chapter 20:Recursive Hyperspectral Band Processing of Fast Iterative Pixel Purity Index Chapter 21:     ConclusionsGlossaryAppendix AReferencesIndex