期刊
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
卷 58, 期 11, 页码 7654-7671出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TGRS.2020.2983063
关键词
Hyperspectral (HS) image; image fusion; nonlocal tensor decomposition; spatial enhancement; spectral unmixing
类别
资金
- National Key Research and Development Program of China [2018YFB1402600]
- National Natural Science Foundation of China [11971374, 11501440, 61603292, 91846110, 61876203]
- Fundamental Research Funds for the Central Universities [xjj2018085]
- MoE-CMCC Artifical Intelligence Project [MCM20190701]
Hyperspectral (HS) imaging has shown its superiority in many real applications. However, it is usually difficult to obtain high-resolution (HR) HS images through existing imaging techniques due to the hardware limitations. To improve the spatial resolution of HS images, this article proposes an effective HS-multispectral (HS-MS) image fusion method by combining the ideas of nonlocal low-rank tensor modeling and spectral unmixing. To be more precise, instead of unfolding the HS image into a matrix as done in the literature, we directly represent it as a tensor, then a designed nonlocal Tucker decomposition is used to model its underlying spatial-spectral correlation and the spatial self-similarity. The MS image serves mainly as a data constraint to maintain spatial consistency. To further reduce the spectral distortions in spatial enhancement, endmembers, and abundances from the spectral are used for spectral regularization. An efficient algorithm based on the alternating direction method of multipliers (ADMM) is developed to solve the resulting model. Extensive experiments on four HS image data sets demonstrate the superiority of the proposed method over several state-of-theart HS-MS image fusion methods.
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