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Remote Sens. 2017, 9(12), 1224;

Joint Local Abundance Sparse Unmixing for Hyperspectral Images

1,2,†,* and 1,†
Faculty of Environmental Engineering, The University of Kitakyushu, Kitakyushu 808-0135, Japan
Faculty of Engineering, Universitas Indonesia, Depok, Jawa Barat 16424, Indonesia
This paper is partially based on the authors’ conference paper, which is presented at the 2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA), Jeju, Korea, 13–16 December 2016.
Author to whom correspondence should be addressed.
Received: 20 October 2017 / Revised: 14 November 2017 / Accepted: 22 November 2017 / Published: 27 November 2017
(This article belongs to the Special Issue Hyperspectral Imaging and Applications)
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Sparse unmixing is widely used for hyperspectral imagery to estimate the optimal fraction (abundance) of materials contained in mixed pixels (endmembers) of a hyperspectral scene, by considering the abundance sparsity. This abundance has a unique property, i.e., high spatial correlation in local regions. This is due to the fact that the endmembers existing in the region are highly correlated. This implies the low-rankness of the abundance in terms of the endmember. From this prior knowledge, it is expected that considering the low-rank local abundance to the sparse unmixing problem improves estimation performance. In this study, we propose an algorithm that exploits the low-rank local abundance by applying the nuclear norm to the abundance matrix for local regions of spatial and abundance domains. In our optimization problem, the local abundance regularizer is collaborated with the L 2 , 1 norm and the total variation for sparsity and spatial information, respectively. We conducted experiments for real and simulated hyperspectral data sets assuming with and without the presence of pure pixels. The experiments showed that our algorithm yields competitive results and performs better than the conventional algorithms. View Full-Text
Keywords: sparse unmixing; hyperspectral; local abundance; nuclear norm sparse unmixing; hyperspectral; local abundance; nuclear norm

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

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Rizkinia, M.; Okuda, M. Joint Local Abundance Sparse Unmixing for Hyperspectral Images. Remote Sens. 2017, 9, 1224.

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