Hyperspectral Data Processing: Algorithm Design and Analysis

18.1 Introduction

In traditional two-dimensional (2D) image processing, an image pixel is specified by its intensity and represented by a single value of the gray scale. In hyperspectral image processing, a hyperspectral image is an image cube formed by stacking 2D spectral images acquired by a range of hundreds of spectral channels where a hyperspectral image pixel is actually a column vector, of which each vector component is an image pixel acquired by a specific wavelength. To simplify our discussion, the term “pixel” will be used instead of “pixel vector.” Therefore, one great challenge in hyperspectral data exploitation is analysis of information extracted from a hyperspectral image pixel specified by hundreds of spectral channels. However, how much pixel information can be extracted is also determined by what algorithm to be used for information extraction, such as algorithms in PART II (Chapters 7–11) developed for endmember extraction and algorithms in PART III (Chapters 12–17) developed for target detection and classification. In other words, what we are interested in is, “Does an algorithm really do what it is designed for?” For example, in endmember extraction, do pixel purity index (PPI) in Section 7.2.1 and N-finder algorithm (N-FINDR) in Section 7.2.4 really extract pure signatures as they are designed for? In unsupervised target detection, do algorithms, such as automatic target generation process (ATGP) developed by Ren and Chang (2003) in Section 8.5.1 and unsupervised ...