Super-Resolution for Remote Sensing Images via Local–Global Combined Network

Abstract

Super-resolution is an image processing technology that recovers a high-resolution image from a single or sequential low-resolution images. Recently deep convolutional neural networks (CNNs) have made a huge breakthrough in many tasks including super-resolution. In this letter, we propose a new single-image super-resolution algorithm named local-global combined networks (LGCNet) for remote sensing images based on the deep CNNs. Our LGCNet is elaborately designed with its “multifork” structure to learn multilevel representations of remote sensing images including both local details and global environmental priors. Experimental results on a public remote sensing data set (UC Merced) demonstrate an overall improvement of both accuracy and visual performance over several state-of-the-art algorithms.