PySRResNet: Super Resolution for Video Satellite Imagery via Pyramid Residual Network
Man Xiao, Zhi He, Jiemin Wu · 2020
Video satellite is of great significance in change detection and military reconnaissance due to its high temporal resolution. However, restricted by the hardware conditions, spatial resolution must be sacrificed if temporal resolution is to be guaranteed. Because of this, how to reconstruct super resolution (SR) video satellite data is particularly important. Based on the proposed SR Residual Network (SRResNet), we proposed a Pyramid Residual Network (PySRResNet) model, using a pyramid structure to obtain features of different scales, including 1, 1/2 and 1/4, and concatenate them together to provide more detailed information for SR reconstruction. In addition, we reduced the number of blocks and removed the batch normalization layer to achieve good performance. Training with “Jilin-1” video satellite images, our PySRResNet can get superior grades than other comparing models both in PSNR and SSIM, which demonstrates the effectiveness of PySRResNet in video satellite imagery SR reconstruction.