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Spectral unmixing based on nonnegative matrix factorization with local smoothness constraint
conference contribution
posted on 2015-01-01, 00:00 authored by Z Yang, L Yang, Z Cai, Yong XiangYong XiangSpectral unmixing (SU) is an emerging problem in the remote sensing image processing. Since both the endmember signatures and their abundances have nonnegative values, it is a natural choice to employ the attractive nonnegative matrix factorization (NMF) methods to solve this problem. Motivated by that the abundances are sparse, the NMF with local smoothness constraint (NMF-LSC) is proposed in this paper. In the proposed method, the smoothness constraint is utilized to impose the sparseness, instead of the traditional L1-norm which is restricted by the underlying column-sum-to-one requirement of the to the abundance matrix. Simulations show the advantages of our algorithm over the compared methods.
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Event
IEEE China Summit and International Conference on Signal and Information Processing (2015 : Chengdu, China)Pagination
635 - 638Publisher
IEEELocation
Chengdu, ChinaPlace of publication
Piscataway, N.J.Publisher DOI
Start date
2015-07-12End date
2015-07-15ISBN-13
9781479919482Language
engPublication classification
E Conference publication; E1 Full written paper - refereedCopyright notice
2015, IEEETitle of proceedings
ChinaSIP 2015: Proceedings of the IEEE China Summit and International Conference on Signal and Information ProcessingUsage metrics
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