A complexity constrained nonnegative matrix factorization for hyperspectral unmixing

Authors:
Sen Jia;Yuntao Qian
Affiliations:
College of Computer Science, Zhejiang University, Hangzhou, P.R. China;College of Computer Science, Zhejiang University, Hangzhou, P.R. China
Venue:
ICA'07 Proceedings of the 7th international conference on Independent component analysis and signal separation
Year:
2007

Citing 4
Cited 0

Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications

Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications
Hyperspectral Imaging: Techniques for Spectral Detection and Classification

Hyperspectral Imaging: Techniques for Spectral Detection and Classification
Nonsmooth Nonnegative Matrix Factorization (nsNMF)

IEEE Transactions on Pattern Analysis and Machine Intelligence
Blind Source Separation Using Temporal Predictability

Neural Computation

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Abstract

Hyperspectral unmixing, as a blind source separation (BSS) problem, has been intensively studied from independence aspect in the last few years. However, independent component analysis (ICA) can not totally unmix all the materials out because the sources (abundance fractions) are not statistically independent. In this paper a complexity constrained nonnegative matrix factorization (CCNMF) for simultaneously recovering both constituent spectra and correspondent abundances is proposed. Three important facts are exploited: First, the spectral data are nonnegative; second, the variation of the material spectra and abundance images is smooth in time and space respectively; third, in most cases, both of the material spectra and abundances are localized. Experimentations on real data are provided to illustrate the algorithm's performance.