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. Front Matter....Pages i-xxiii Introduction....Pages 1-28 Front Matter....Pages 29-29 Simplex Volume Calculation....Pages 31-47 Discrete-Time Kalman Filtering for Hyperspectral Processing....Pages 49-71 Target-Specified Virtual Dimensionality for Hyperspectral Imagery....Pages 73-119 Front Matter....Pages 121-121 Real-Time Recursive Hyperspectral Sample Processing for Active Target Detection: Constrained Energy Minimization....Pages 123-156 Real-Time Recursive Hyperspectral Sample Processing for Passive Target Detection: Anomaly Detection....Pages 157-205 Front Matter....Pages 207-208 Recursive Hyperspectral Sample Processing of Automatic Target Generation Process....Pages 209-226 Recursive Hyperspectral Sample Processing of Orthogonal Subspace Projection....Pages 227-259 Recursive Hyperspectral Sample Processing of Linear Spectral Mixture Analysis....Pages 261-287 Recursive Hyperspectral Sample Processing of Maximum Likelihood Estimation....Pages 289-317 Recursive Hyperspectral Sample Processing of Orthogonal Projection-Based Simplex Growing Algorithm....Pages 319-356 Recursive Hyperspectral Sample Processing of Geometric Simplex Growing Algorithm....Pages 357-396 Front Matter....Pages 397-398 Recursive Hyperspectral Band Processing for Active Target Detection: Constrained Energy Minimization....Pages 399-420 Recursive Hyperspectral Band Processing for Passive Target Detection: Anomaly Detection....Pages 421-447 Front Matter....Pages 449-450 Recursive Hyperspectral Band Processing of Automatic Target Generation Process....Pages 451-481 Recursive Hyperspectral Band Processing of Orthogonal Subspace Projection....Pages 483-503 Recursive Hyperspectral Band Processing of Linear Spectral Mixture Analysis....Pages 505-527 Recursive Hyperspectral Band Processing of Growing Simplex Volume Analysis....Pages 529-542 Recursive Hyperspectral Band Processing of Iterative Pixel Purity Index....Pages 543-594 Recursive Band Processing of Fast Iterative Pixel Purity Index....Pages 595-625 Front Matter....Pages 449-450 Conclusions....Pages 627-651 Back Matter....Pages 653-690 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. Explores recursive structures in algorithm architecture Implements algorithmic recursive architecture in conjunction with progressive sample and band processing Derives Recursive Hyperspectral Sample Processing (RHSP) techniques according to Band-Interleaved Sample/Pixel (BIS/BIP) acquisition format Develops Recursive Hyperspectral Band Processing (RHBP) techniques according to Band SeQuential (BSQ) acquisition format for hyperspectral data
