Hyperspectral Data Processing: Algorithm Design and Analysis

VI

Hyperspectral Signal Coding

The second category of this book, Category B, is devoted to hyperspectral signal processing. The question is for a given data sample vector treated as a signature vector without reference to others, what is the best possible we can do to explore as much spectral information across the entire wavelength range to specify the data sample vector for spectral characterization. There are two ways to process hyperspectral signals either in a discrete manner as discrete signal processing referred to as signal coding in Part VI or in a continuous manner as continuous signal processing referred to as signal characterization in Part VII.

In Part VI, the focus is placed on hyperspectral signal coding that represents a signature vector by a discrete vector as a code word that can be considered as its fingerprint. A simplest way to accomplish this task is binary coding that binarizes each component of a signature vector for its identification. An earliest attempt was made by Mazer et al. (1988) to develop a so-called spectral analysis manager (SPAM) that encoded a remote sensing image data sample vector into a binary code vector for signature discrimination, classification, and identification. It calculates spectral mean and interband spectral difference and uses them as thresholds to generate a binary code word for a given hyperspectral signature vector. The SPAM binary coding was then extended to a spectral feature-based binary coding (SFBC) developed by Qian ...