Hyperspectral Data Processing: Algorithm Design and Analysis

8.5 Linear Spectral Mixture Analysis-Based SQ-EEAs

In Section 7.2.4, a linear spectral mixture analysis (LSMA)-based SM-EEA is developed on the basis of LSE, called FCLS-EEA, where a set of endmembers are found simultaneously by maximizing LSE among all possible subsets that contain the same number of samples. Since all the endmembers must be found simultaneously, full abundance constraints, ASC and ANC, should be imposed on the searching process. However, in a case of successive endmember extraction, there is no need for imposing both ASC and ANC on SQ-EEAs. So, in this section, three second-order statistics-based SQ-EEAs will be presented, all of which are LSE-based spectral unmixing techniques with/without abundance constraints.

The first LSMA-based SQ-EEA of interest is an unconstrained-abundance least-squares algorithm, called ATGP developed by Ren and Chang (2003), which makes use of a sequence of OSP to find target sample vectors successively. In other words, ATGP extracts endmembers from a sequence of nested orthogonal subspaces with reduced dimensionality. It can be considered an unsupervised OSP (UOSP) that extends OSP developed by Harsanyi and Chang (1994) to an unsupervised version of OSP (Chang et al., 1998a; Chang, 2003a). A second LSMA based SQ-EEA is an unsupervised abundance nonnegativity constrained least-squares algorithm, called UNCLS, which is based on nonnegativity constrained least-squares (NCLS) developed by Chang and Heinz (2000). Since it also uses OSP ...