Super-Resolution Reconstruction of Remote Sensing Images Using Generative Adversarial Network With Shallow Information Enhancement
Yujia Fu, Xiangrong Zhang, Mingyang Wang · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · 2022
The super-resolution reconstruction method based on deep learning can significantly improve the spatial super-resolution of remote sensing images. However, the current methods make insufficient use of the remote context information and channel information in shallow feature extraction, resulting in the limited effect of super-resolution reconstruction. This paper proposed a new super-resolution reconstruction model, SIEGAN, which uses generative adversarial network with shallow information enhancement to improve the effect of super-resolution reconstruction of remote sensing images. Similar to other generative adversarial models, SIEGAN is composed of generator and discriminator. But SIEGAN enhances the generator's ability to extract shallow information by using three different scale convolution operations. Specifically, a depth-wise convolution is used to extract the local context information of each band of the image. A depth-wise dilation convolution is used to capture the remote context information in the image. Finally, a 1×1 convolution is used to extract the correlation features between different channels in remote sensing images. In addition, SIEGAN uses U-Net network as its discriminator to provide detailed feedback per pixel to the generator, to improve the model's ability to identify image details. And the spectral-spatial total variation loss function is introduced to ensure the spectral-spatial reliability of the reconstructed images. The experimental results on Gaofen-1 data proved that compared with the state-of-the-art models, SIEGAN has achieved better super-resolution reconstruction performance. Furthermore, the reconstructed images by SIEGAN demonstrate better performance in land cover classification.