Hyperspectral Data Processing: Algorithm Design and Analysis

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Simultaneous Endmember Extraction Algorithms (SM-EEAs)

Endmembers provide fundamental understanding of hyperspectral data where an endmember is defined as an idealized pure signature used to specify a particular spectral class. With the advent of recently developed hyperspectral imaging sensors, which utilize hundreds of contiguous spectral channels with significantly improved spatial and spectral resolutions, it is now possible to find endmembers, an important and crucial task in hyperspectral data exploitation. On many occasions endmembers appear as anomalies, rare substances, small unidentified targets, which cannot be resolved by multispectral imaging sensors but in fact provide vital information. Over the past few years, many endmember extraction algorithms (EEAs) have been developed and reported in the public domain. One of the early developments in endmember extraction is pixel purity index (PPI) developed by Boardman (1994). Since then it has emerged as one of the most widely used EEAs due to its availability in the environment for visualizing images (ENVI) commercialized by the analytical imaging and geophysics (AIG) (Research Systems Inc., 2001). In addition to PPI, many other EEAs have also been developed, for example, minimum-volume transform (MVT) (Craig, 1994), convex cone analysis (CCA) (Ifarraguerri and Chang, 1999), N-finder algorithm (N-FINDR) (Winter, 1999), automated morphological endmember extraction (AMEE) algorithm (Plaza et al., 2002), iterative error ...