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This book explores recursive architectures in designing progressive hyperspectral imaging algorithms. In particular, it makes progressive imaging algorithms recursive by introducing the concept of Kalman filtering in algorithm design so that hyperspectral imagery can be processed not only progressively sample by sample or band by band but also recursively via recursive equations. This book can be considered a companion book of author's books, Real-Time Progressive Hyperspectral Image Processing , published by Springer in 2016.
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 (JohnWiley & 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.
Overview and Introduction.- PART I: Fundamentals.- Simplex Volume Calculation.- Discrete Time Kalman Filtering in Hyperspectral Data Prcoessing.- Target-Specified Virtual Dimesnionality.- PART II: Sample Spectral Statistics-Based Recursive Hyperspectral Sample Prcoessing.- Real Time Recursive Hyperspectral Sample Processing of Constrained Energy Minimization.- Real Time Recursive Hyperspectral Sample Processing of Anomaly Detection.- PART III: Signature Spectral Statistics-Based Recursive Hyperspectral Sample Prcoessing.- Recursive Hyperspectral Sample Processing of Automatic Target Generation Process.- Recursive Hyperspectral Sample Processing of Orthogonal Subspace Projection.- Recursive Hyperspectral Sample Processing of Linear Spectral Mixture Analysis.- Recursive Hyperspectral Sample Processing of Maximimal Likelihood Estimation.- Recursive Hyperspectral Sample Processing of Orthogonal Projection-Based Simplex Growing Algorithm.- Recursive Hyperspectral Sample Processing of Geometric Simplex Growing Simplex Algorithm.- PART IV: Sample Spectral Statistics-Based Recursive Hyperspectral Band Prcoessing.- Recursive Hyperspectral Band Processing of Constrained Energy Minimization.- Recursive Hyperspectral Band Processing of Anomly Detection.- Signature Spectral Statistics-Based Recursive Hyperspectral Band Prcoessing.- Recursive Hyperspectral Band Processing of Automatic Target Generation Process.- Recursive Hyperspectral Band Processing of Orthogonal Subspce Projection.- Recursive Hyperspectral Band Processing of Linear Spectral Mixture Analysis.- Recursive Hyperspectral Band Processing of Growing Simplex Volume Analysis.- Recursive Hyperspectral Band Processing of Iterative Pixel Puirty Index.- Recursive Hyperspectral Band Processing of Fast Iterative Pixel Purity Index.- Conclusions.- Glossary.- Appendix A.- References.- Index.
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