Hyperspectral Data Processing: Algorithm Design and Analysis

1.3 Divergence of Hyperspectral Imagery from Multispectral Imagery

The hyperspectral imagery has changed the way we think of multispectral imagery. This is because we now have hundreds of contiguous spectral bands available at our disposal. So, one major issue is how to effectively use and take advantage of spectral information provided by these hundreds of spectral bands for various applications in data exploitation, for example, target detection, discrimination, classification, quantification, and identification. This interesting issue can be addressed by the following two interesting examples. The first example uses real-to-complex analysis to illustrate why it is inappropriate to simply extend multispectral imaging techniques to process hyperspectral imagery. The second example uses the well-known pigeon-hole principle in discrete mathematics (Epp, 1995) to illustrate how hyperspectral imagery can be addressed by a rationale completely different from that used for multispectral imagery.

1.3.1 Misconception: Hyperspectral Imaging is a Natural Extension of Multispectral Imaging

While dealing with hyperspectral imagery there is a general consensus that hyperspectral imagery is a natural extension of multispectral imagery based on an assumption that a hyperspectral image has more spectral bands for data collection than a multispectral image does. As a result, it may lead to a misconception that hyperspectral imaging problems can be solved by multispectral imaging techniques by ...