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Overview
Hyperspectral Imaging: Techniques for Spectral Detection and Classification is an outgrowth of the research conducted over the years in the Remote Sensing Signal and Image Processing Laboratory (RSSIPL) at the University of Maryland, Baltimore County. It explores applications of statistical signal processing to hyperspectral imaging and further develops non-literal (spectral) techniques for subpixel detection and mixed pixel classification. This text is the first of its kind on the topic and can be considered a recipe book offering various techniques for hyperspectral data exploitation. In particular, some known techniques, such as OSP (Orthogonal Subspace Projection) and CEM (Constrained Energy Minimization) that were previously developed in the RSSIPL, are discussed in great detail. This book is self-contained and can serve as a valuable and useful reference for researchers in academia and practitioners in government and industry.
Synopsis
Offering a range of techniques for hyperspectral data exploitation, this reference focuses on applications of statistical signal processing for hyperspectral image analysis—specifically subpixel detection and mixed-pixel classification. The volume is based on research by Chang and his students in the Remote Sensing Signal and Image Processing Laboratory at the U. of Maryland. Topics include orthogonal subspace projection (OSP), least-squares methods for target abundance-constrained subpixel detection, a quantitative analysis of mixed-to-pure pixel conversion (MPCV), and linear spectral random mixture analysis (LSRMA). Annotation ©2003 Book News, Inc., Portland, OR