NDLI: Adaptive endmember extraction based sparse nonnegative matrix factorization with spatial local information

Content Provider	IEEE Xplore Digital Library
Author	Huali Li Shutao Li Liangpei Zhang
Copyright Year	2015
Description	Author affiliation: State Key Lab. of Inf. Eng. in Surveying, Wuhan Univ., Wuhan, China (Liangpei Zhang) \|\| Coll. of Electr. & Inf. Eng., Hunan Univ., Changsha, China (Huali Li; Shutao Li)
Abstract	Hyperspectral Unmixing aims at getting the endmember signature and their corresponding abundance maps from highly mixed Hyperspectral image. Nonnegative Matrix Factorization (NMF) is a widely used method for spectral unmixing because it can obtain better performance while there is no pure pixels in the hyperspectral image. However, many methods based on nonnegative matrix factorization seldom consider the spatial information both on local and nonlocal. To combine the spatial and spectral information together to improve the unmixing accuracy, an adaptive endmember extraction based sparse nonnegative matrix factorization with spatial local information (ASNMF) is proposed in this paper. A superpixel segmentation is to obtain many meaningful regions which are spectral similar and spatial adjacent. Endmember is adaptively extracted on each superpixel to generate endmember set. Initialing the endmember set, ASNMF could adaptively obtain the final endmembers with the sparse nonnegative matrix factorization. Both the experiments on synthetic and real scene images show the effectiveness of the proposed method for hyperspectral unmixing.
Starting Page	1753
Ending Page	1756
File Size	461925
Page Count	4
File Format	PDF
e-ISBN	9781479979295
DOI	10.1109/IGARSS.2015.7326128
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-07-26
Publisher Place	Italy
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Sparse matrices Hyperspectral imaging Image segmentation Data mining Mathematical model hyperspectral remote sensing imagery adaptive endmember extraction sparse nonnegative matrix factorization
Content Type	Text
Resource Type	Article

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