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

  • Beihang University

科研成果: 书/报告/会议事项章节会议稿件同行评审

摘要

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.

源语言英语
主期刊名Proceedings - 2011 7th International Conference on Natural Computation, ICNC 2011
1836-1840
页数5
DOI
出版状态已出版 - 2011
活动2011 7th International Conference on Natural Computation, ICNC 2011 - Shanghai, 中国
期限: 26 7月 201128 7月 2011

出版系列

姓名Proceedings - 2011 7th International Conference on Natural Computation, ICNC 2011
4

会议

会议2011 7th International Conference on Natural Computation, ICNC 2011
国家/地区中国
Shanghai
时期26/07/1128/07/11

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