Hyperspectral and Multispectral Image Fusion via Nonlocal Low-Rank Tensor Approximation and Sparse Representation

Xuelong Li; Yue Yuan; Qi Wang

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Hyperspectral and Multispectral Image Fusion via Nonlocal Low-Rank Tensor Approximation and Sparse Representation

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摘要

The fusion of hyperspectral (HS) and multispectral (MS) images designed to obtain high-resolution HS (HRHS) images is a very challenging work. A series of solutions has been proposed in recent years. However, the similarity in the structure of the HS image has not been fully used. In this article, we present a novel HS and MS image-fusion method based on nonlocal low-rank tensor approximation and sparse representation. Specifically, the HS image and the MS image are considered the spatially and spectrally degraded versions of the HRHS image, respectively. Then, the nonlocal low-rank constraint term is adopted in order to form the nonlocal similarity and the spatial–spectral correlation. Meanwhile, we add the sparse constraint term to describe the sparsity of abundance. Thus, the proposed fusion model is established and its optimization is solved by alternative direction method of multipliers (ADMM). The experimental results on three synthetic data sets and one real data set show the advantages of the proposed method over several state-of-the-art competitors.

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《IEEE Transactions on Geoscience and Remote Sensing》 |2021年第1期|550-562|共13页
作者
Xuelong Li; Yue Yuan; Qi Wang;
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作者单位

School of Computer Science, Northwestern Polytechnical University, Xi’an, China;

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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; Bayes methods; Spatial resolution; Sparse matrices; Machine learning; Image fusion; Hyperspectral (HS) image; image fusion; low-rank tensor approximation; multispectral (MS) image; sparse representation;

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Hyperspectral and Multispectral Image Fusion via Nonlocal Low-Rank Tensor Approximation and Sparse Representation

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