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Real-Time Recursive Hyperspectral Sample and Band Processing: Algorithm Architecture and Implementation de Chein-I Chang

Real-Time Recursive Hyperspectral Sample and Band Processing: Algorithm Architecture and Implementation

Autor Chein-I Chang
en Limba Engleză Paperback – 8 mai 2018
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.

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Specificații

ISBN-13: 9783319832302
ISBN-10: 3319832301
Pagini: 690
Ilustrații: XXIII, 690 p. 293 illus., 233 illus. in color.
Dimensiuni: 155 x 235 mm
Greutate: 1.33 kg
Ediția:Softcover reprint of the original 1st ed. 2017
Editura: Springer International Publishing
Colecția Springer
Locul publicării:Cham, Switzerland

Cuprins

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.

Notă biografică

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.

Textul de pe ultima copertă

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

Caracteristici

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