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

ISBN-13: 9783319832302 / Angielski / Miękka / 2018 / 690 str.

Chein-I Chang

Spis treści

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

Glossary

Appendix A

References

Index

O autorze

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.

Blurb

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