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Proceedings Paper

Semi-supervised hyperspectral unmixing approach based on nonnegative matrix factorization
Author(s): Lifu Zhang; Nan Wang; Xia Zhang; Zhengfu Chen; Min Gao
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Paper Abstract

Non-negative matrix factorization (NMF) has been introduced into the field of hyperspectral unmixing in the last ten years. Though NMF-based approaches have been widely accepted by researchers, the assumptions in them may not always fit for the characteristics of real ground objectives, which will cause the incorrect results and restrict the applications for these approaches. This paper proposes a novel semi-supervised NMF model, in which the ground truth information is introduced such as partial known endmembers from ground measurment. The relationship between the known and unknown endmembers are explored. The distance function is designed to describe the relationship and introduced into the NMF model. In this way, SSNMF could use the known endmembers to help estimating the unknown endmembers, so that accurate and robust results can be obtained. The proposed algorithm was compared with NMFupk, which also considered partial known endmembers, using extensive synthetic data and real hyperspectral data. The experiments show that the proposed algorithm can give a better performance.

Paper Details

Date Published: 19 May 2016
PDF: 9 pages
Proc. SPIE 9874, Remotely Sensed Data Compression, Communications, and Processing XII, 987408 (19 May 2016); doi: 10.1117/12.2225465
Show Author Affiliations
Lifu Zhang, Institute of Remote Sensing and Digital Earth (China)
Nan Wang, Institute of Remote Sensing and Digital Earth (China)
Xia Zhang, Institute of Remote Sensing and Digital Earth (China)
Zhengfu Chen, Jiangsu UMap Spatial Information Technology Co., Ltd. (China)
Min Gao, Jiangsu UMap Spatial Information Technology Co., Ltd. (China)


Published in SPIE Proceedings Vol. 9874:
Remotely Sensed Data Compression, Communications, and Processing XII
Bormin Huang; Chein-I Chang; Chulhee Lee, Editor(s)

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