Hyperspectral Data Processing: Algorithm Design and Analysis

12.1 Introduction

Hyperspectral imagery provides additional benefits over multispectral imagery in many applications, such as detection, discrimination, classification, quantification, identification, etc. In early days, hyperspectral imagery has been processed and analyzed by multispectral image processing algorithms via preprocessing such as feature extraction, dimensionality reduction, and band selection. Such multispectral-to-hyperspectral approaches have achieved some success and may have led to a brief that hyperspectral imaging is nothing more than a straightforward extension of multispectral image processing. As we will see, this is apparently not the case. When the spectral resolution is low as multispectral images are, the used image processing techniques are generally developed to explore spatial information such as geographical information system (GIS) (Jensen, 1996) for spatial domain analysis. Therefore, as spectral resolution is increased significantly like hyperspectral imagery, such spatial domain-based multispectral imaging techniques may be found to be less effective in certain applications. In particular, if targets of interest only account for a small population with very limited spatial extent, the techniques based on spatial information can easily break down. In some cases where the target size may be even smaller than the pixel resolution, for example, rare minerals in geology, special species in agriculture and ecology, small vehicles in battlefields, etc., ...