## 5 Discussion  
### Pros and Cons of CSWGAN  
Currently, there is a need for satellite data with higher spatial and temporal resolution than what can be provided by individual imaging sensors, such as Sentinel-2 or GaoFen-2, in order to better understand surface changes and their causal factors. To address this, this study proposed a deep learning super-resolution reconstruction method called CSWGAN based on a cascaded structure, which downscaled Sentinel-2 images to a spatial resolution of 4 m, providing richer spatial details within a short time interval. The experimental results showed that, compared to representative works of different network structures, SRCNN and ESPCN, although having simple structures, showed a weaker performance for images with complex texture information. ESRT can achieve relatively good results in terms of metrics; however, it still tended to lose certain high-frequency information, and the perceptual quality of the reconstructed images still needs improvement. SRGAN  
Figure 9: Evaluation histogram of high-frequency characteristic images with different reconstruction results. (**a**) and (**b**), (**c**) and (**d**), (**e**) and (**f**) are the results of pictures 1–3, respectively. and ESRGAN performed relatively well in both perceptual and objective metrics. The CSWGAN, unlike other single-structure networks, leveraged a cascading structure that can harness the advantages of multiple individual networks, yielding superior perceptual performance and high-quality images. The proposed SGAN and WGAN have also been proven to show good results in the spatial and frequency domains, respectively. The experimental results demonstrated that the cascaded network structure combining spatial- and frequency-domain information can leverage the advantages of multiple single networks, resulting in superior perceptual performance and generating high-quality images. As the demand for precision increases, there are a growing number of networks in the field of computer vision with powerful image representation capabilities [44; 45; 46]. In the future, it is hoped that further research will be conducted on how to transfer these models to the application of remote sensing images. Loss function, as an important component guiding the network towards the correct direction, plays a crucial role in the allocation of coefficients for different loss functions to enhance the performance of the network. It is essential to scientifically and efficiently balance different loss terms to optimize the performance. Many studies on multi-task learning networks have proposed learning methods for loss function weights [47; 48], and future attempts will also be made to refer to these algorithms to further improve the loss function. In addition, the preliminary experimental results on network architecture still focus on displaying the RGB channels. However, this is limited for remote sensing images with multi-band characteristics [49]. For example, the vegetation red edge band of Sentinel-2 can effectively detect vegetation health [50], and the coastal/aerosol band (B1) can be used for monitoring water vapor and aerosol corrections [51; 52]. Nevertheless, the spatial resolution of these bands cannot meet the actual application requirements. Therefore, the study of the super-reconstruction of multi-band cascaded spatial-frequency-domain network models will be part of future work. In contrast to [PERSON] et al. 's [53], we find that conducting experiments on the RGB channels first is beneficial because most multi-band reconstruction methods are built upon the high-resolution RGB channels. Finally, there are significant resolution differences among different sensors, such as a 3\(\times\) resolution difference between Landsat-7 and Sentinel-2, and a 2\(\times\) resolution difference between GaoFen-1 and GaoFen-2. In such cases, our model may require additional training to meet the super-resolution needs of different sensors.