# Panchromatic and Hyperspectral Image Fusion Using Ratio Residual Attention Networks
###### Abstract  
Hyperspectral remote sensing images provide rich spectral information about land surface features and are widely used in fields such as environmental monitoring, disaster assessment, and land classification. However, effectively leveraging the spectral information in hyperspectral images remains a significant challenge. In this paper, we propose a hyperspectral pansharpening method based on ratio transformation and residual networks, which significantly enhances both spatial details and spectral fidelity. The method generates an initial image through ratio transformation and refines it using a residual attention network. Additionally, specialized loss functions are designed to preserve both spatial and spectral details. Experimental results demonstrate that, when evaluated on the EO-1 and Chikusei datasets, the proposed method outperforms other methods in terms of both visual quality and quantitative metrics, particularly in spatial detail clarity and spectral fidelity. This approach effectively addresses the limitations of existing technologies and shows great potential for high-resolution remote sensing image processing applications. p +
Footnote †: journal: Applied sciences  
article  
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Among these traditional model-based methods, Coupled Non-negative Matrix Factorization (CNMF) [7] and Hyperspectral Super-resolution (HySure) [8] stand out as representative approaches. CNMF jointly decomposes panchromatic and hyperspectral image matrices to effectively integrate spatial and spectral information, enhancing fusion quality; HySure utilizes a convex optimization framework that incorporates prior knowledge and data fidelity terms to improve spectral preservation. These methods have advanced beyond basic transform-based fusion by better preserving details and reducing artifacts, laying the groundwork for subsequent deep learning approaches. However, as research has advanced, the limitations of these methods have become increasingly evident. On the one hand, to ensure the feasibility of subsequent feature fusion, traditional methods require the same transformation to extract features from different source images. However, this approach does not account for the feature differences across source images, which may lead to poor feature representation. On the other hand, traditional feature fusion strategies are often too coarse, resulting in limited fusion performance. In the last decade, the rise of deep learning has brought about significant changes in the field of image processing. Deep learning technologies have also been successfully applied to panchromatic and hyperspectral image fusion, addressing many challenges that traditional methods could not overcome. [PERSON] et al. [9] proposed a fusion method that combines guided filtering and residual networks for panchromatic and hyperspectral image fusion. [PERSON] et al. [10] introduced a spectral detail-preserving convolutional neural network for fusion. [PERSON] et al. [11] proposed a dense network based on Laplacian pyramids (DenseNet) for the fusion of panchromatic and hyperspectral images. In addition to the deep learning methods combined with traditional techniques mentioned above, many novel deep learning approaches have emerged in the field of panchromatic and hyperspectral image fusion. For example, [PERSON] et al. [12] designed a 3D generative adversarial network for hyperspectral image fusion. [PERSON] et al. [13] proposed a hyperspectral-panchromatic sharpening method based on deep priors and a dual-attention residual network. In addition to earlier deep learning-based approaches, several recent methods have significantly advanced hyperspectral pansharpening since 2020. [PERSON] et al. [14] proposed an unsupervised fusion method based on a low-rank diffusion model, which improves structural fidelity without requiring paired supervision. [PERSON] et al.