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Proceedings Paper

Lagrange constraint neural network for fully constrained subpixel classification in hyperspectral imagery
Author(s): Hsuan Ren; Harold H. Szu; James R. Buss
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Paper Abstract

Linear unmixing approaches are used to estimate the abundance fractions of the endmembers resident in each pixel. Generally, two constraints will be applied. First, the abundance fractions of each endmembers should be nonnegative, which is called nonnegativity constraint. The second constraint, called sum-to-one constraint, says the sum of all abundance fractions should be one. One great challenge is to include the nonnegativity constraint while solving linear mixture model. In this paper, we propose a Lagrange constraint neural network (LCNN) approach to linearly unmix the spectrum with both sum-to-one and nonnegativity constraints.

Paper Details

Date Published: 8 March 2002
PDF: 7 pages
Proc. SPIE 4738, Wavelet and Independent Component Analysis Applications IX, (8 March 2002); doi: 10.1117/12.458766
Show Author Affiliations
Hsuan Ren, U.S. Army Edgewood Chemical and Biological Ctr. (United States)
Harold H. Szu, George Washington Univ. (United States)
James R. Buss, Office of Naval Research (United States)

Published in SPIE Proceedings Vol. 4738:
Wavelet and Independent Component Analysis Applications IX
Harold H. Szu; James R. Buss, Editor(s)

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