Hyperspectral Data Processing (eBook)

Hyperspectral Data Processing: Algorithm Design and
Analysis is a culmination of the research conducted in the
Remote Sensing Signal and Image Processing Laboratory (RSSIPL) at
the University of Maryland, Baltimore County. Specifically, it
treats hyperspectral image processing and hyperspectral signal
processing as separate subjects in two different categories. Most
materials covered in this book can be used in conjunction with the
author's first book, Hyperspectral Imaging: Techniques for
Spectral Detection and Classification, without much
overlap.

Many results in this book are either new or have not been
explored, presented, or published in the public domain. These
include various aspects of endmember extraction, unsupervised
linear spectral mixture analysis, hyperspectral information
compression, hyperspectral signal coding and characterization, as
well as applications to conceal target detection, multispectral
imaging, and magnetic resonance imaging. Hyperspectral Data
Processing contains eight major sections:

* Part I: provides fundamentals of hyperspectral data
processing

* Part II: offers various algorithm designs for endmember
extraction

* Part III: derives theory for supervised linear spectral mixture
analysis

* Part IV: designs unsupervised methods for hyperspectral image
analysis

* Part V: explores new concepts on hyperspectral information
compression

* Parts VI & VII: develops techniques for hyperspectral
signal coding and characterization

* Part VIII: presents applications in multispectral imaging and
magnetic resonance imaging

Hyperspectral Data Processing compiles an algorithm
compendium with MATLAB codes in an appendix to help readers
implement many important algorithms developed in this book and
write their own program codes without relying on software
packages.

Hyperspectral Data Processing is a valuable reference for
those who have been involved with hyperspectral imaging and its
techniques, as well those who are new to the subject.