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

by Chein-I Chang

Overview

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.

Product Details

ISBN-13:	9783319451718
Publisher:	Springer-Verlag New York, LLC
Publication date:	04/23/2017
Sold by:	Barnes & Noble
Format:	eBook
Pages:	690
File size:	22 MB
Note:	This product may take a few minutes to download.

About the Author

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.

Table of Contents

Chapter 1: Overview and Introduction
PART I: Fundamentals 
Chapter 2: Simplex Volume Calculation
Chapter 3: Discrete Time Kalman Filtering in Hyperspectral Data Prcoessing
Chapter 4: Target-Specified Virtual Dimesnionality
PART II: Sample Spectral Statistics-Based Recursive Hyperspectral Sample Prcoessing 
Chapter 5: Real Time Recursive Hyperspectral Sample Processing of Constrained Energy Minimization
Chapter 6: Real Time Recursive Hyperspectral Sample Processing of Anomaly Detection
PART III: Signature Spectral Statistics-Based Recursive Hyperspectral Sample Prcoessing 
Chapter 7: Recursive Hyperspectral Sample Processing of Automatic Target Generation Process
Chapter 8: Recursive Hyperspectral Sample Processing of Orthogonal Subspace Projection
Chapter 9: Recursive Hyperspectral Sample Processing of Linear Spectral Mixture Analysis
Chapter 10: Recursive Hyperspectral Sample Processing of Maximimal Likelihood Estimation
Chapter 11: Recursive Hyperspectral Sample Processing of Orthogonal Projection-Based Simplex Growing Algorithm
Chapter 12: Recursive Hyperspectral Sample Processing of Geometric Simplex Growing Simplex Algorithm
PART 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 Detection
PART 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:     Conclusions
GlossaryAppendix AReferencesIndex