NDLI: Parallel optimization of hyperspectral unmixing based on sparsity constrained nonnegative matrix factorization

Content Provider	IEEE Xplore Digital Library
Author	Zebin Wu Shun Ye Jie Wei Jianjun Liu Zhihui Wei Le Sun
Copyright Year	2013
Description	Author affiliation: Sch. of Comput. Sci. & Technol., NJUST, Nanjing, China (Zebin Wu; Shun Ye; Jie Wei; Jianjun Liu; Zhihui Wei; Le Sun)
Abstract	Hyperspectral unmixing is a typical problem of blind source separation, which can be solved by nonnegative matrix factorization (NMF). Sparsity based NMF will increase the efficiency of unmixing, but its computational complexity limits the possibility of utilizing it in time-critical applications. In this paper, method of parallel hyperspectral unmixing based on sparsity constrained nonnegative matrix factorization on Graphics Processing Units (CSNMF-GPU) is investigated and compared in terms of both accuracy and speed. The realization of the proposed method using Compute Unified Device Architecture (CUDA) on GPU are described and evaluated. The experimental results comparing with the serial implementations based on both simulated and real hyperspectral data demonstrate the effectiveness of the proposed parallel optimization approach.
Sponsorship	IEEE Geosci. Remote Sens. Soc.
Starting Page	1438
Ending Page	1441
File Size	333855
Page Count	4
File Format	PDF
ISBN	9781479911141
ISSN	21536996
DOI	10.1109/IGARSS.2013.6723055
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-07-21
Publisher Place	Australia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Hyperspectral imaging Graphics processing units Sparse matrices Algorithm design and analysis Kernel Optimization nonegative matrix factorization hyperspectral unmixing parallel optimization sparsity
Content Type	Text
Resource Type	Article

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