HYPERSPECTRAL UNMIXING ALGORITHM BASED ON NONNEGATIVE MATRIX FACTORIZATION

被引：3
|
作者：
Bao, Wenxing [1 ]
Li, Qin [1 ]
Xin, Liping [1 ]
Qu, Kewen [1 ]
机构：
[1] Beifang Univ Nationalities, Sch Comp Sci & Engn, Yinchuan 750021, Peoples R China
来源：
2016 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS) | 2016年
基金：
中国国家自然科学基金;
关键词：
Hyperspectral unmixing; Nonnegative Matrix Factorization (NMF); local linear embedding (LLE); NeNMF;
D O I：
10.1109/IGARSS.2016.7730821
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
Nonnegative Matrix Factorization (NMF) factorizes a nonnegative matrix into product of two positive matrixes, which is widely used in hyperspectral unmixing. However, the convergence speed of NMF is comparatively slower, and a large number of local minimum will be existed when it is directly adopted in the factorization of hyperspectral image mixed pixels. A modified hyperspectral unmixing method based on NMF is presented in this paper. The local linear embedding (LLE) algorithm is used to reduce the dimension of hyperspectral data. The sparseness and smoothness constraints are added into the cost function. The NeNMF algorithm is used in updating endmember matrix and abundance matrix for hyperspectral data. The results show that this method can achieve good result of classification.
引用
收藏
页码：6982 / 6985
页数：4
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