Multispectral and Hyperspectral Image Fusion Based on Coupled Non-Negative Block Term Tensor Decomposition with Joint Structured Sparsity

• Published in: IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium pp. 7376 - 7379
• Main Authors: Guo, Hao, Bao, Wenxing, Feng, Wei, Sun, Shasha, Yang, Lei, Qu, Kewen, Ma, Xuan, Zhang, Xiaowu
• Format: Conference Proceeding
• Language: English
• Published: IEEE 16.07.2023
• Subjects: Block term tensor; Couplings; Estimation; Geoscience and remote sensing; Image fusion; Image reconstruction; Matrix decomposition; Structured sparse; Tensor decomposition; Tensors
• ISSN: 2153-7003
• DOI: 10.1109/IGARSS52108.2023.10282402

Multispectral and hyperspectral image fusion (MHF) aims to reconstruct high-resolution hyperspectral images by fusing spatial and spectral information. The block-item tensor fusion model is able to use endmember and abundance information to improve the quality of hyperspectral images. This paper implements image fusion based on a coupled non-negative block term tensor decomposition model. Firstly, the two abundance matrices are formed into a chunking matrix and L 2,1 -parametric is added as well, promoting structured sparsity and eliminating the scaling effect present in the model. Immediately after, the counter-scaling effect present in the model is eliminated by adding a L 2 -parametric number to the endmember matrix. Finally, the focus is on solving the noise/artifacts generated by the no exact estimation of rank in the model, and over-estimation of rank by coupling the chunking matrix and the endmember matrix together to reconstruct the matrix, adding L 2,1 -parameters to it to facilitate the elimination of chunks, and solving the problems using an extended iteratively reweighted least squares (IRLS) method. The experiments on the University of Pavia dataset show that the proposed algorithm works better compared to the state of the art methods.