Hybrid-Scale Self-Similarity Exploitation for Remote Sensing Image Super-Resolution

Recently, deep convolutional neural networks (CNNs) have made great progress in remote sensing image super-resolution (SR). The CNN-based methods can learn powerful feature representation from plenty of low- and high-resolution counterparts. For remote sensing images, there are many similar ground targets recurred inside the image itself, both within the same scale and across different scales. In this article, we argue that this internal recurrence can be used for learning stronger feature representation, and we propose a new hybrid-scale self-similarity exploitation network (HSENet) for remote sensing image SR. Specifically, we introduce a single-scale self-similarity exploitation module (SSEM) to compute the feature correlation within the same scale image. Moreover, we design a cross-scale connection structure (CCS) to capture the recurrences across different scales. By combining SSEM and CCS, we further develop a hybrid-scale self-similarity exploitation module (HSEM) to construct the final HSENet, which simultaneously exploits single- and cross-scale similarities. Experimental results demonstrate that HSENet can obtain superior performance over several state-of-the-art methods. Besides, the effectiveness of our method is also verified by the assistance to the remote sensing scene classification task.