# Panchromatic and Hyperspectral Image Fusion Using Ratio Residual Attention Networks
###### Abstract
[15] introduced MFT-GAN, a multi-scale feature-guided transformer network that integrates GANs and transformer architectures to enhance spectral-spatial representation. [PERSON] and [PERSON] [16] developed HyperTransformer, a fusion framework that combines both textural and spectral cues using a transformer-based design to achieve high-fidelity results. Furthermore, [PERSON] et al. [17] proposed PSBT, a pyramid shuffle-and-reshuffle transformer that effectively captures both local and global dependencies via a hierarchical attention mechanism. Compared to these recent approaches, our method maintains a simpler residual attention architecture while achieving competitive or superior performance in both spatial detail preservation and spectral fidelity. These deep learning-based panchromatic and hyperspectral image fusion methods typically use original hyperspectral (HS) images as labels, along with degraded panchromatic (PANLR) images and hyperspectral images (HSLR) as training samples due to the lack of high-resolution hyperspectral images for supervision during training. While these methods can achieve certain results, they overlook the specific objective of panchromatic sharpening, treating it merely as an image regression problem. They treat the panchromatic sharpening process as a black-box deep learning problem, failing to account for various real-world factors, which leads to insufficient retention of spectral and spatial information. Furthermore, using blurred or downsampled images as training samples may result in the loss of fused image information, particularly with respect to preserving spectral and spatial details. Since hyperspectral images encompass a wide spectral range and a large number of narrow-band details, existing methods are prone to spectral distortion and spatial blurring, ultimately degrading the quality of the fused image. These real-world challenges are primarily caused by the following three technical problems:  
1. Supervised deep learning fusion methods require high-resolution hyperspectral images as labels, but such high-resolution hyperspectral images are not available in practice. 2. The fusion problem of panchromatic and hyperspectral images is treated as a black-box deep learning problem, where deep networks learn the mapping relationship between high-resolution and low-resolution images. 3. Spectral differences exist between the two images, and many continuous narrow-band details need to be analyzed, making it easy to introduce spectral distortion and spatial blurring. To address the above issues, this paper proposes a fusion method for panchromatic and hyperspectral images that combines ratio transformations and residual networks. First, a ratio transformation method is applied to generate an initial ratio image. Next, the ratio image is input into a residual attention network, which fine-tunes the ratio image and produces a new ratio image. Finally, the generated ratio image is multiplied by the hyperspectral upsampled image to obtain the final fused image. During this process, two loss functions are designed: the spectral preservation loss function and the spatial preservation loss function, which separately constrain the spectral and spatial details of the fused image. This method does not require blurring or downsampling of the original hyperspectral images to prepare training data, effectively preserving both the spectral and spatial details of the original hyperspectral and panchromatic images. The structure of this paper is organized as follows: Section 2 introduces the proposed fusion method and its implementation. Section 3 presents the experimental results and discusses the performance of the proposed method. Section 4 provides a discussion of the advantages and limitations of the method. Finally, Section 5 concludes the paper and summarizes its main contributions.