Hyperspectral and Multispectral Image Fusion via Logarithmic Low-Rank Tensor Ring Decomposition

Autor: Jun Zhang, Lipeng Zhu, Chengzhi Deng, Shutao Li
Jazyk: angličtina
Rok vydání: 2024
Předmět:
Zdroj: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 17, Pp 11583-11597 (2024)
Druh dokumentu: article
ISSN: 1939-1404
2151-1535
DOI: 10.1109/JSTARS.2024.3416335
Popis: Integrating a low-spatial-resolution hyperspectral image with a high-spatial-resolution multispectral image (HR-MSI) is recognized as a valid method for acquiring HR-HSI. Among the current fusion approaches, the tensor ring (TR) decomposition-based method has received growing attention owing to its superior performance in preserving the spatial-spectral correlation. Based on the TR decomposition, the degradation model is developed via the spectral and spatial cores in TR. Here, we study the low-rankness of TR factors from the TNN perspective and consider the mode-2 logarithmic TNN (LTNN) on each TR factor. A novel fusion model is proposed by incorporating this LTNN regularization and the weighted total variation which is to promote the continuity of HR-HSI in the spatial-spectral domain. Meanwhile, we have devised a proximal alternating minimization algorithm to solve the proposed model. The experimental results indicate that our method improves the visual quality and exceeds the existing state-of-the-art fusion approaches concerning various quantitative metrics.
Databáze: Directory of Open Access Journals