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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 Xiang
Spectral 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.

History

Event

IEEE China Summit and International Conference on Signal and Information Processing (2015 : Chengdu, China)

Pagination

635 - 638

Publisher

IEEE

Location

Chengdu, China

Place of publication

Piscataway, N.J.

Start date

2015-07-12

End date

2015-07-15

ISBN-13

9781479919482

Language

eng

Publication classification

E Conference publication; E1 Full written paper - refereed

Copyright notice

2015, IEEE

Title of proceedings

ChinaSIP 2015: Proceedings of the IEEE China Summit and International Conference on Signal and Information Processing