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 or at subpixel level with targets embedded in a single pixel vector due to their size that is smaller than the ground sampling distance (GSD). Besides, these types of targets usually appear unexpectedly and their probabilities of occurrence are also low. Most importantly, their sample pool may also be relatively small and their sizes may only have limited spatial extent. As a consequence, such targets may not be easy to be visually identified or inspected with prior knowledge; thus, they can be considered as insignificant targets but are indeed of major interest from an intelligence or information point of view. For example, these targets may include special spices in agriculture and ecology, toxic wastes in environmental monitoring, rare minerals in geology, drug/smuggler trafficking in law enforcement, military vehicles in combat, abnormality in battlefields, landmines in war zones, chemical/biological agents in bioterrorism, weapon concealment and mass graves in intelligence gathering, and so on. Under such circumstances, they can be only detected at mixed or subpixel level and traditional spatial domain (i.e., literal)-based image processing techniques may not be suitable or effective. So, the extraction of such targets must rely on their spectral profiles and the techniques developed for hyperspectral image analysis should perform target-based detection, discrimination, classification, identification, recognition, and quantification as opposed to pattern-based multispectral imaging techniques. As a result, a direct extension of multispectral imaging techniques to hyperspectral imagery may not be effective in hyperspectral data exploitation because pattern class information and correlation provided by these targets may be too little to be used for performing hyperspectral image analysis. In order to address this issue the techniques in Chang (2003a) are developed directly from a hyperspectral imagery point of view for spectral detection and classification. This book expands the scope of Chang (2003a) to cover a wider range of applications including endmember extraction, unsupervised target detection, information compression, and hyperspectral signal coding and characterization, none of which is studied in Chang (2003a).