# Satellite Image Super-Resolution by 2D RRDB and Edge-Enhanced Generative Adversarial Network +
###### Abstract  
With the gradually increasing demand for high-resolution images, image super-resolution (SR) technology has become more and more important in our daily life. In general, high resolution is often accomplished by increasing the accuracy and density of the sensor. However, such an approach is too expensive on the design and equipment. Particularly, increasing the sensor density of satellites incurs great risks. Inspired by EEGAN, some parts of networks: Ultra-Dense Subnet (UDSN) and Edge-Enhanced Subnet (EESN) are modified. The UDSN is used to extract features and obtain high-resolution images which look clear but are deteriorated by artifacts in the intermediate stage, while the EESN is used to purify, enhance and extract the image contours and uses mask processing to eliminate the image corrupted by noise. Then, the restored intermediate image and the enhanced edge are combined to become a high-resolution image with clear contents and high authenticity. Finally, we use Kaggle open source, AID, WHU-RS19, and SpaceWill datasets to perform the test and compare the SR results among different models. It shows that our proposed approach outperforms other state-of-the-art SR models on satellite images. generated image is just an enlarged LR image or a real HR image. After training with sufficient data and time, the generator is able to output synthetic (fake) HR images which are supposed to be similar to real HR images, while the discriminator is not capable of distinguishing between fake and true images. The satellite imagery covers a wide area, including various ground scenes. In addition, the resolution of satellite images is much lower than general images, and it is also susceptible to several effects such as ultra-telephoto imaging, atmospheric disturbances, and equipment noise. This further increases the difficulty on the edge restoration with details and sharpness from LR input images. Therefore, the conspicuous outline of the ground target is more worth pursuing than the actual texture details inside the object. In the past few years, various shallow and deep learning-based reconstruction methods have been proposed to improve the resolution of satellite images. Especially, the residual learning strategy is applied to build a deeper CNN for computer vision tasks and makes the results amazing. For the image SR problem, these methods aim to predict the residual image (relative to the input image) rather than the target HR image, and the methods based on residual learning and their variants have been proven effective. Although the image generated looks very realistic, the image content is excessively eroded due to the global optimization strategy. As a result, the SR image may be inconsistent with the actual HR image and a large number of false or smooth edges may appear. We are inspired by the EEGAN [5] and EESRGAN [6] and use EEGAN as the basic framework to propose a feasible infrastructure. For the two sub-networks in the generator, a combination of residual-in-residual dense block and two-dimensional topology (i.e., 2D-RRDB) is used. Compared with the conventional RRDB [4], this 2D architecture with additional diagonal connections can lead to better gradient optimization on the links between different routes and provide more possibilities for information conversion. In other words, more connected paths can be obtained using the same number of layers through the diagonal links. That means by increasing the density of dense blocks connection over the traditional 1D infrastructure, we can effectively overcome the information propagation disappearance, gradient disappearance and training difficulty problems caused by increasing the layer depth. Moreover, in the light of the use of the same approach to compute the perceptual loss for the entire image (e.g., we use the same features on the foreground, background, and edges), the proposed model needs to include new losses and learn the information for smaller features (e.g., the texture of the building).