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Hyperspectral unmixing using non-negative matrix factorization with automatically estimating regularization parameters

  • Beihang University

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Hyperspectral unmixing is a process by which pixel spectra in a scene are decomposed into constituent materials and their corresponding fractions. Nonnegative matrix factorization (NMF) is a method recently developed to deal with matrix factorization. This paper proposes a hyperspectral unmixing algorithm using auto-NMF based on the L-curve theory. It is an approach to automatically estimate regularization parameters, which are manually chosen subjectively and difficultly in the traditional regularized non-negative matrix factorization (RNMF). We experiment traditional algorithms and auto-NMF on the synthetic data, better results are obtained from auto-NMF, indicating it is an effective technique for hyperspectral unmixing.

Original languageEnglish
Title of host publicationProceedings - 2011 7th International Conference on Natural Computation, ICNC 2011
Pages1836-1840
Number of pages5
DOIs
StatePublished - 2011
Event2011 7th International Conference on Natural Computation, ICNC 2011 - Shanghai, China
Duration: 26 Jul 201128 Jul 2011

Publication series

NameProceedings - 2011 7th International Conference on Natural Computation, ICNC 2011
Volume4

Conference

Conference2011 7th International Conference on Natural Computation, ICNC 2011
Country/TerritoryChina
CityShanghai
Period26/07/1128/07/11

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