A Hybrid Registration and Fusion Method for Hyperspectral Super-resolution

• URL: http://arxiv.org/abs/2407.05279v1
• Date: Sun, 7 Jul 2024 06:36:19 GMT
• Title: A Hybrid Registration and Fusion Method for Hyperspectral Super-resolution
• Authors: Kunjing Yang, Minru Bai, TingLu,
• Abstract summary: We propose a hybrid registration and fusion model named RAF-NLRGS. The RAF model incorporates a constrained group sparsity to harness valuable information embedded in the residual data group. We also present the framework of Generalized Gauss-Newton (GGN) algorithm and Proximal Alternating Optimization (PAO)
• Score: 0.913509220304172
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Fusing hyperspectral images (HSIs) with multispectral images (MSIs) has become a mainstream approach to enhance the spatial resolution of HSIs. Many HSI-MSI fusion methods have achieved impressive results. Nevertheless, certain challenges persist, including: (a) A majority of current methods rely on accurate registration of HSI and MSI, which can be challenging in real-world applications.(b) The obtained HSI-MSI pairs may not be fully utilized. In this paper, we propose a hybrid registration and fusion constrained optimization model named RAF-NLRGS. With respect to challenge (a), the RAF model integrates batch image alignment within the fusion process, facilitating simultaneous execution of image registration and fusion. To address issue (b), the NLRGS model incorporates a nonconvex low-rank and group-sparse structure, leveraging group sparsity to effectively harness valuable information embedded in the residual data. Moreover, the NLRGS model can further enhance fusion performance based on the RAF model. Subsequently, the RAF-NLRGS model is solved within the framework of Generalized Gauss-Newton (GGN) algorithm and Proximal Alternating Optimization (PAO) algorithm. Theoretically, we establish the error bounds for the NLRGS model and the convergence analysis of corresponding algorithms is also presented. Finally, extensive numerical experiments on HSI datasets are conducted to verify the effectiveness of our method.

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