Dynamic Multi-Scale Network for Remote Sensing Image Super-Resolution

Ping Yao, Peng He, Siyuan Cheng, Li Fu, Zhihao Guo, Jianghong Zhao

Abstract: Although single image super-resolution(SISR) method with deep neural network has already been explored in depth in natural images, further research on SISR in remote sensing image is still desired as aerial imagery has distinctive characteristics such as varied scenes across wide areas. In this paper, considering the scarcity of remote sensing images that may restrict the performance of data-driven neural network, we first propose a new data augmentation method, RotBlur, to promote sample diversity dramatically with a rotated cropped block at random angle. And we also design a Dynamic Multi-Scale Network(DMSN) to enhance details of low-resolution(LR) remote sensing images adaptively according to current scene of various images. Experiments performed on the UC Merced Land-Use dataset demonstrate that our DMSN outperforms several state-of-the-art methods in terms of PSNR of reconstructed images.