Hyperspectral Data Processing: Algorithm Design and Analysis

31.1 Introduction

It is known that a hyperspectral imager can discriminate and quantify materials more effectively via much better spectral resolution than a multispectral imager can. However, an interesting issue seems to be overlooked and has never been addressed, “how to define and differentiate hyperspectral imagery from multispectral imagery”. Until we can settle this issue, the algorithm design for hyperspectral imagery cannot be effective. This has been the case over the past years where hyperspectral imagery has been considered and viewed as a natural extension of multispectral imagery under a common sense that hyperspectral imagery has more spectral bands with finer resolutions than multispectral imagery with low spectral resolution. With this intuitive generalization, in early days a general approach to designing HSI algorithms has been the one that extends algorithms developed for multipsectral imagery in a straightforward fashion. One of such techniques is the maximum likelihood-based classification and estimation (Landgrebe, 2003). Unfortunately, using this multispectral-to-hyperspectral extension may not be a best way to design algorithms for hyperspectral image analysis as already discussed and addressed in Chapter 1 (Section 1.2). We believe that one of main causes may be due to the fact that there is no specific criterion or definition that can be used to distinguish a hyperspectral image from a multispectral image in a rigorous and mathematical means. Because ...