###### Abstract
In recent years, due to its advantage of a self-attention mechanism, the transformer model has become an essential choice in the field of natural language processing. However, the complexity of the transformer model has posed a significant challenge to its performance. To address this issue, an Efficient Super-Resolution Transformer (ESRT) is proposed. The ESRT aims to enhance the ability of SR networks to capture long-range contextual dependencies while significantly reducing the GPU memory cost [31]. However, due to the rich land coverage and complex land use scenes contained in remote sensing images within a limited pixel size, the loss of high-frequency detail information is more severe. It is difficult to effectively reconstruct the lost high-frequency information by focusing only on spatial-domain information. Restoring high-frequency information through power spectrum features in the frequency domain appears simpler and more effective. Wavelet transform combines temporal and frequency resolutions, adapting window sizes and facilitating detail feature extraction. It can process high-frequency information such as edges and details while reducing computational memory [32; 33; 34]. In DWSR, residual networks and wavelet transforms exploit the residual sparsity of wavelet coefficients. Different wavelet subbands, providing complementary structural information in different directions, predict the HR image [35]. WRAN [36] uses a multi-kernel convolutional to adaptively aggregate features of the receptive fields from different-sized receptive fields of wavelet coefficients. Nevertheless, the multi-band image information of different sensors is complex and degraded and there are few studies on the frequency-domain multi-scale reconstruction of image details. Combining wavelet transform and the residual structure of GANs ensures network gradients and extracts deeper features [37]. In addition, loss constraint in the wavelet domain retains the advantages of wavelet transform in high-frequency information reconstruction and avoids excessive smoothing of reconstructed images [38]. However, most of the existing algorithms extract the spatial- and frequency-domain features using a single network. Deepening or widening the network improves algorithm performance, it also increases computational complexity, and at the same time, it is difficult for these methods to effectively couple spatial and frequency information. Therefore, there is a need to develop a new network model that can maintain the advantages of extracting spatial information while enhancing the ability to extract frequency-domain information. So far, although many SR methods based on remote sensing have been developed, it has been challenging to acquire paired high-resolution and low-resolution images for training, leading to a lack of corresponding \"high-resolution and low-resolution\" real remote sensing image pairs. Additionally, most models are trained on natural image datasets, like DIV2K, UC_Merced [39], and WHU-RS19 [40], which results in significant performance degradation when applied to real remote sensing image SR tasks [41]. To better address the degradation information between HR and LR remote sensing images, this study focuses on developing a benchmark paired remote sensing image dataset considering the following four critical features: (1) different sensors, (2) diversity of regions, (3) consistency of time, and (4) challenging scenarios. Based on the four aforementioned aspects, we propose a new benchmark dataset GF_Sen, formed by images obtained from GaoFen-2 and Sentinel-2. The main objectives of this study are as follows: (1) To propose a cascaded spatial frequency-domain generative adversarial network CSWGAN, specifically for cross-sensor remote sensing images reconstruction. We first proposed the SGAN and WGAN, which integrate the advantages of a self-attention mechanism and wavelet transform, enriching the global feature information and high-frequency details in the reconstructed image.