Hyperspectral image fusion via weighted nuclear norm regularized sparse matrix factorization

Jingjing Lu; Mingxi Ma

doi:10.1117/12.2682386

20 June 2023 Hyperspectral image fusion via weighted nuclear norm regularized sparse matrix factorization

Jingjing Lu, Mingxi Ma

Proceedings Volume 12715, Eighth International Conference on Electronic Technology and Information Science (ICETIS 2023); 1271502 (2023) https://doi.org/10.1117/12.2682386
Event: Eighth International Conference on Electronic Technology and Information Science (ICETIS 2023), 2023, Dalian, China

Abstract

The fusion of a low spatial resolution hyperspectral image (LR-HSI) and a high spatial resolution multispectral image (HR-MSI) in the same scene is a common method to get a high spatial resolution hyperspectral image (HR-HSI). For the drawback that the standard nuclear norm regularization treats each singular value equally, this paper proposes a weighted nuclear norm model based on sparse matrix factorization (called WNNS) for hyperspectral image fusion. Specifically, we promote the sparsity of fused images by adding the ℓ₁norm of coefficients. Furthermore, to preserve important data components, we combine with the weighted nuclear norm regularization, where different weights are given to singular values. To efficiently solve the proposed model, we apply an alternating direction method of multipliers (ADMM). Experiments show that the proposed method has better performances in terms of numerical results and visual effects.

Citation Download Citation

Jingjing Lu and Mingxi Ma "Hyperspectral image fusion via weighted nuclear norm regularized sparse matrix factorization", Proc. SPIE 12715, Eighth International Conference on Electronic Technology and Information Science (ICETIS 2023), 1271502 (20 June 2023); https://doi.org/10.1117/12.2682386

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