Enhancing Hyperspectral Unmixing With Two-Stage Multiplicative Update Nonnegative Matrix Factorization

Li Sun; Kang Zhao; Congying Han; Ziwen Liu

doi:10.1109/ACCESS.2019.2955984

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Enhancing Hyperspectral Unmixing With Two-Stage Multiplicative Update Nonnegative Matrix Factorization

Journal article

Open access

Peer reviewed

Enhancing Hyperspectral Unmixing With Two-Stage Multiplicative Update Nonnegative Matrix Factorization

Li Sun, Kang Zhao, Congying Han and Ziwen Liu

IEEE access, Vol.7, pp.171023-171031

01/01/2019

DOI: 10.1109/ACCESS.2019.2955984

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Abstract

Nonnegative matrix factorization (NMF) is a powerful tool for hyperspectral unmixing (HU). This method factorizes a hyperspectral cube into constituent endmembers and their fractional abundances. In this paper, we propose a two-stage nonnegative matrix factorization algorithm. During the first stage, k-means clustering is first employed to obtain the estimated endmember matrix. This matrix serves as the initial matrix for NMF during the second stage, where we design a new cost function for the purpose of refining the solutions of NMF. The two-stage NMF model is solved with multiplicative update rules, and the monotonic convergence of this algorithm is proven with an auxiliary function. Numerical tests demonstrate that our two-stage NMF algorithm can achieve accurate and stable solutions.

Computer Science

Computer Science, Information Systems

Engineering

Engineering, Electrical & Electronic

Science & Technology

Technology

Telecommunications

Details

Title: Subtitle: Enhancing Hyperspectral Unmixing With Two-Stage Multiplicative Update Nonnegative Matrix Factorization
Creators: Li Sun - Shandong Agricultural University
Kang Zhao - University of Iowa
Congying Han - University of Chinese Academy of Sciences
Ziwen Liu - Univ Chinese Acad Sci, Sch Math Sci, Beijing 100049, Peoples R China
Resource Type: Journal article
Publication Details: IEEE access, Vol.7, pp.171023-171031
DOI: 10.1109/ACCESS.2019.2955984
ISSN: 2169-3536
eISSN: 2169-3536
Publisher: IEEE
Number of pages: 9
Grant note: 11701337; 41271235; 11301307 / National Science Foundation of China; National Natural Science Foundation of China (NSFC) J16LI16 / Project of Shandong Province Higher Educational Science and Technology Program ZR2016DM03 / Natural Science Foundation, Shandong, China; National Natural Science Foundation of China (NSFC)
Language: English
Date published: 01/01/2019
Academic Unit: Business Analytics
Record Identifier: 9984380534802771

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