###### Abstract  
Due to the multi-scale and spectral features of remote sensing images compared to natural images, there are significant challenges in super-resolution reconstruction (SR) tasks. Networks trained on simulated data often exhibit poor reconstruction performance on real low-resolution (LR) images. Additionally, compared to natural images, remote sensing imagery involves fewer high-frequency components in network construction. To address the above issues, we introduce a new high-low-resolution dataset GF_Sen based on GaoFen-2 and Sentinel-2 images and propose a cascaded network CSWGAN combined with spatial-frequency features. Firstly, based on the proposed self-attention GAN (SGAN) and wavelet-based GAN (WGAN) in this study, the CSWGAN combines the strengths of both networks. It not only models long-range dependencies and better utilizes global feature information, but also extracts frequency content differences between different images, enhancing the learning of high-frequency information. Experiments have shown that the networks trained based on the GF_Sen can achieve better performance than those trained on simulated data. The reconstructed images from the CSWGAN demonstrate improvements in the PSNR and SSIM by 4.375 and 4.877, respectively, compared to the relatively optimal performance of the ESRGAN. The CSWGAN can reflect the reconstruction advantages of a high-frequency scene and provides a working foundation for fine-scale applications in remote sensing. super-resolution; remote sensing; cross-sensor; generative adversarial network; cascade structure +
Footnote †: journal: Medical Imaging  
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learning has shown promising results in SR. Convolution neural networks (CNNs) have made significant progress in image SR [11; 12; 13; 14; 15; 16; 17]. SRCNN produces clearer reconstructed images and significantly improves the reconstruction speed [18]. ESPCN accelerates feature learning and reduces network complexity by directly passing feature information in the LR space [19]. However, with the increasing demand for reconstruction results, the depth of the network is also increasing, leading to the problem of vanishing gradients. The residual structure has gradually played a role in SR tasks. It allows CNNs to model more deeply without suffering from vanishing/exploding gradients [11]. Subsequently, some heavyweight models have been proposed [13; 16; 20; 21; 22]. However, due to excessive focus on minimizing the mean square reconstruction error, the reconstruction results are \"overly smooth\" [23; 24; 25]. Generative adversarial networks (GANs) have been introduced to image SR by leveraging adversarial learning between the generator and discriminator to generate high-quality results. For example, SRGAN demonstrates the ability of GANs in inverse imaging problems, which combines multiple loss functions to ensure that the generated results have both good perceptual and objective quality [26]. ESRGAN improves reconstruction by addressing noise susceptibility and enhancing residual blocks with residual dense blocks [27]. In the field of remote sensing, inspired by the natural image reconstruction method, research based on the characteristics of remote sensing images has also been carried out. [PERSON] et al. had addressed the challenges of large-scale remote sensing images by combining dense sampling and residual learning methods to enhance the effectiveness of feature extraction [28]. Despite the benefits of convolutions in modeling image dependencies, their ability to learn long-term dependencies is limited. To address this problem, the self-attention mechanism is introduced to carefully coordinate the details of each position of the image with the details of the distant part of the image [29]. Combining the self-attention mechanism with GANs allows for the extraction of global features without introducing excessive parameters [30].