Preface
Hyperspectral imaging has witnessed tremendous growth over the past few years. Still its applications to new areas are yet to be explored. Many hyperspectral imaging techniques have been developed and reported in various venues. My first book, Hyperspectral Imaging: Techniques for Spectral Detection and Classification, referenced as Chang (2003a), was written in an attempt to summarize the research conducted at that time in my laboratory (remote sensing signal and image processing laboratory, RSSIPL) and to provide readers with a peek of this fascinating and exciting area. With rapid advancement in this area many signal processing techniques have been developed for hyperspectral signal and image processing. This book has been written with four goals in mind. One is to continuously explore new statistical signal processing algorithms in this area for various applications. Many results in this book are new, particularly some in Chapters 2, 4, 5–6, 11, 16, 18–19, 23, 24, 29, 30–31, and 33. A second goal is to supplement Chang (2003a), where many potential research efforts were only briefly mentioned (in Chapter 18 of the book). A third goal is to distinguish this book from Chang (2003a) in many ways. Unlike Chang (2003a) where the main theme was hyperspectral target detection and classification from a viewpoint of subpixel and mixed pixel analysis, this book is focused on more in-depth treatment of hyperspectral signal and image processing from a statistical signal processing ...
