## 4 Experiments  
### Dataset and Settings  
Although there are a large number of remote sensing satellites operating in space, the remote sensing image datasets are far less complete than the set of natural images due to national security, data confidentiality, and other reasons. At the same time, existing remote sensing image datasets are not fully suitable for super-resolution tasks. On the one hand, most of them were made for target detection tasks. This has resulted in a limited variety of ground object detail contained in the images in the datasets. These datasets contained a large number of remotely sensed images of cars, aircraft, and boats, but lacked remotely sensed images of areas such as buildings, vegetation, and water surfaces. On the other hand, some datasets were produced in earlier years, and the limited resolution of remote sensing images made it difficult to provide effective supervision for super-resolution tasks. Therefore, a new super-resolution dataset was produced based on the latest Google Earth images.  
The new dataset comprises remote sensing images of urban centers, suburban areas, grasslands, farmlands, forests, water, and other regions. Remote sensing images of city centers and suburbs in Barcelona, New York, London, and Berlin were gathered. Images of grassland and farmland areas in the plains of Western and Eastern Europe were also collected. Furthermore, images of forested areas from South America and water areas from the Caribbean Sea were obtained. The acquisition of remote sensing images primarily focused on detail-dense areas to thoroughly test the detail recovery capability of the super-resolution network. These images were cropped to a size of \(256\times 256\) and were downsampled in multiple levels to obtain supervision images at different levels before being fed into the deep network training period. After pre-processing, our new dataset contains 4000 training images and 410 test images.  
In order to adequately train and test the proposed super-resolution method and existing methods, the UCMerced dataset and the AID dataset were also used. The UCMerced dataset was produced in 2010 for scene classification by the University of California, Merced. The images were obtained from the US National Geological Survey's National Map Urban Areas image collection. It contains 21 categories of scenes, each containing 100 remote sensing images. The dataset was widely used in training and testing of networks for land use type classification, target detection, etc. The AID dataset was a large dataset collected from Google Earth for scene classification in 2017, produced by Wuhan University and Huazhong University of Science and Technology. It contained thirty categories of scenes, each containing between 200 and 400 images. Considering the large number of images in the AID dataset and the GPU memory limitation, images containing complex textures were chosen as the training set, which contains about 4000 images.  
Considering the increase in parameter quantity brought by multi-level networks, parameter quantization and network pruning were adopted during the training process [40; 41; 42; 43]. This approach greatly reduced the network size and computational complexity while preserving the original network performance. Both our method and the existing method used as a comparison were trained and tested on an Ubuntu 18.04 server with an RTX2080 GPU. The server has a cuda version of 10.0 and a pytorch version of 1.6.0.