Endmember Estimation with Maximum Distance Analysis

Endmember estimation plays a key role in hyperspectral image unmixing, often requiring an of the number endmembers and extracting endmembers. However, most existing extraction algorithms require prior knowledge regarding endmembers, being critical process during unmixing. To bridge this, new maximum distance analysis (MDA) method is proposed that simultaneously estimates spectral signatures without any information on experimental data containing pure pixel no noise, based assumption form simplex with greatest volume over all combinations. The includes farthest point from coordinate origin space, which implies that: (1) other must be endmember, (2) line (3) plane (or affine hull) endmember. Under this scenario, first used to create aforementioned point, line, plane, hull. remaining are extracted by repetitively searching for points satisfy above three assumptions. In addition behaving as endmember algorithm itself, MDA can co-operate techniques via generalizing them into more effective schemes. conducted experiments validate effectiveness efficiency our synthetic real data.