Hyperspectral and Multispectral Image Fusion via Graph Laplacian-Guided Coupled Tensor Decomposition

Yuanyang Bu; Yongqiang Zhao; Jize XueJonathan Cheung-Wai ChanSeong G. KongChen YiJinhuan WenBinglu Wang

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Hyperspectral and Multispectral Image Fusion via Graph Laplacian-Guided Coupled Tensor Decomposition

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摘要

We propose a novel graph Laplacian-guided coupled tensor decomposition (gLGCTD) model for fusion of hyperspectral image (HSI) and multispectral image (MSI) for spatial and spectral resolution enhancements. The coupled Tucker decomposition is employed to capture the global interdependencies across the different modes to fully exploit the intrinsic global spatial–spectral information. To preserve local characteristics, the complementary submanifold structures embedded in high-resolution (HR)-HSI are encoded by the graph Laplacian regularizations. The global spatial–spectral information captured by the coupled Tucker decomposition and the local submanifold structures are incorporated into a unified framework. The gLGCTD fusion framework is solved by a hybrid framework between the proximal alternating optimization (PAO) and the alternating direction method of multipliers (ADMM). Experimental results on both synthetic and real data sets demonstrate that the gLGCTD fusion method is superior to state-of-the-art fusion methods with a more accurate reconstruction of the HR-HSI.

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《IEEE Transactions on Geoscience and Remote Sensing》 |2021年第1期|648-662|共15页
作者
Yuanyang Bu; Yongqiang Zhao; Jize XueJonathan Cheung-Wai ChanSeong G. KongChen YiJinhuan WenBinglu Wang;
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作者单位

Department of Natural and Applied Sciences, Northwestern Polytechnical University, Xi’an, China;

Department of Automation, Northwestern Polytechnical University, Xi’an, China;

Research and Development Institute of Northwestern Polytechnical University in Shenzhen, Shenzhen, ChinaDepartment of Electronics and Informatics, Vrije Universiteit Brussel, Brussel, BelgiumDepartment of Computer Engineering, Sejong University, Seoul, South Korea;

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正文语种英语
中图分类地球物理学;遥感技术;
关键词
Tensile stress; Matrix decomposition; Sparse matrices; Laplace equations; Manifolds; Hyperspectral imaging; Spatial resolution; Coupled tensor decomposition; graph Laplacian; hyperspectral imaging; image fusion; manifold structure;

Hyperspectral and Multispectral Image Fusion via Graph Laplacian-Guided Coupled Tensor Decomposition

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