Remote Sensing Image Super-Resolution Using Second-Order Multi-Scale Networks
Xiaoyu Dong, Longguang Wang, Xu Sun, Xiuping Jia, Lianru Gao, Bing Zhang
Abstract
Remotely sensed images, especially in urban areas, have highly complex spatial distribution, since the ground objects have diverse ranges of sizes and shapes. This largely increases the difficulty of super-resolution (SR) tasks. Current deep convolutional neural network (CNN)-based SR methods often show limited performance when coping with complicated images. This article develops a second-order multi-scale super-resolution network (SMSR) to explore reconstruction tasks for difficult cases. Specifically, we propose a single-path feature reuse which cleverly captures multi-scale feature information through aggregating the features learned at different depths of a single path. Further, we present a second-order learning mechanism, which double reuses small-difference and large-difference features at local and global levels, makes use of the learned multi-scale information at maximum. The proposed methods achieve multi-scale learning using small-size convolution only, resulting in a lightweight and high-performance SR network. Experimental results show the superiority of our SMSR over state-of-the-art methods in super-resolving complicated image patterns. The effectiveness of SMSR is also demonstrated through its support to object recognition task.