Hyperspectral Data Processing: Algorithm Design and Analysis

33.7 Hyperspectral Signal Processing

The hyperspectral data considered in Chapters 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22,23 are three-dimensional image cubes where each data sample is actually a pixel vector. So, the data processing carried out in this manner can be viewed as hyperspectral image processing in Category A where two types of correlation are of interest. One is correlation provided by data samples in terms of their spatial locations while paying no attention to interband spectral correlation. This is generally referred to as intersample spatial correlation and is commonly used in traditional image processing to develop spatial domain-based algorithms to perform tasks such as edge detection, region growing, clustering, segmentation, etc. Early data processing for remote sensing data, for example, geographical information system (GIS), belongs to such an approach, which can be considered as spatial domain-based data analysis. The other type of correlation is provided by data samples regardless of their spatial location. It is a complete opposite to the above-mentioned intersample spatial correlation and can be referred to as intrasample spectral correlation which has been explored and investigated in great detail in Chapter 17. The key difference between these two types of correlation can be well explained by the following example. Let be a set of ...