A New Sequential Algorithm for Hyperspectral Endmember Extraction

Endmember extraction is an important step in spectral mixture analysis when endmembers are unknown. Endmembers are usually assumed to be pure pixels present in an image scene. Under this circumstance, endmember extraction is to find the most distinctive pixels. To make the searching process more efficient, the sequential forward search (SFS) method is generally used, where the next endmember is determined with a certain criterion based on the currently extracted endmember set. This letter proposes a new criterion which is related to the estimated endmember abundances. Compared to other sequential endmember extraction algorithms, the proposed method can find all the different endmembers faster. This letter also proposes to use the sequential forward floating search method as the substitute of SFS, which can improve the performance of all the sequential endmember extraction algorithms.