Hyperspectral Data Processing: Algorithm Design and Analysis

1.2 Issues of Multispectral and Hyperspectral Imageries

Because of its low spectral resolution a multispectral image pixel vector usually does not have information as rich as a hyperspectral image pixel vector does. In this case, multispectral image processing must rely on image spatial information and correlation to make up insufficient spectral information resulting from a few discrete spectral bands. Therefore, an early development of multispectral image processing has focused on spatial domain-based techniques. However, with recent advent of very high-spectral resolution hyperspectral imaging sensors many material substances that cannot be resolved by multispectral imaging sensors can now be uncovered by hyperspectral imagers for data analysis. As a consequence, targets or objects of interest for multispectral and hyperspectral image analyses are quite different. In multispectral image analysis land cover/land use is often of major interest. Therefore, the developed techniques generally perform pattern classification and analysis in the sense that every single pixel of an image must be classified into one of a number of pattern classes, each of which corresponds to one particular spatial class. On the contrary, the objects of interest in hyperspectral image analysis are usually targets with particular spectral characteristics such as man-made targets, anomalies, or rare targets. The targets of these types generally appear either in a form mixed with a number of material substances ...