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Endmember Estimation with Maximum Distance Analysis by Tao, Xuanwen (author);Paoletti, Mercedes E. (author);Haut, Juan M. (author);Ren, Peng (author);Plaza, Javier (author);Plaza, Antonio (author) is a Environmental Science article available to read on EtoBox.

What is Endmember Estimation with Maximum Distance Analysis about?

Endmember estimation plays a key role in hyperspectral image unmixing, often requiring an estimation of the number of endmembers and extracting endmembers. However, most of the existing extraction algorithms require prior knowledge regarding the number of endmembers, being a critical process during unmixing. To bridge this, a new maximum distance analysis (MDA) method is proposed that simultaneously estimates the number and spectral signatures of endmembers without any prior information on the experimental data containing pure pixel spectral signatures and no noise, being based on the assumption that endmembers form a simplex with the greatest volume over all pixel combinations. The simplex includes the farthest pixel point from the coordinate origin in the spectral space, which implies that: (1) the farthest pixel point from any other pixel point must be an endmember, (2) the farthest pixel point from any line must be an endmember, and (3) the farthest pixel point from any plane (or affine hull) must be an endmember. Under this scenario, the farthest pixel point from the coordinate origin is the first endmember, being used to create the aforementioned point, line, plane, and affin

Who reads Endmember Estimation with Maximum Distance Analysis?

Author

Tao, Xuanwen (author);Paoletti, Mercedes E. (author);Haut, Juan M. (author);Ren, Peng (author);Plaza, Javier (author);Plaza, Antonio (author)

Publisher

MDPI AG

Published

2021

Language

EN

Field

Environmental Science (Physical Sciences)