Journal
INFORMATION SCIENCES
Volume 181, Issue 10, Pages 1787-1803Publisher
ELSEVIER SCIENCE INC
DOI: 10.1016/j.ins.2010.03.022
Keywords
Neural networks: Associative memories; Lattice associative memories; Lattice algebra: affine independence; Lattice independence; Lattice matrices; Hyperspectral image analysis: endmember search; Spectral unmixing; Abundance maps
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Funding
- National Council of Science and Technology (CONACyT) in Mexico [22036]
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In this manuscript we propose a method for the autonomous determination of endmembers in hyperspectral imagery based on recent theoretical advancements on lattice auto-associative memories. Given a hyperspectral image, the lattice algebra approach finds in a single-pass all possible candidate endmembers from which various affinely independent sets of final endmembers may be derived. In contrast to other endmember detection methods. the endmembers found using two dual canonical lattice matrices are geometrically linked to the data set spectra. The mathematical foundation of the proposed method is first described in some detail followed by application examples that illustrate the key steps of the proposed lattice based method. (C) 2010 Elsevier Inc. All rights reserved.
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