Edge-Enhanced GAN for Remote Sensing Image Superresolution

The current superresolution (SR) methods based on deep learning have shown remarkable comparative advantages but remain unsatisfactory in recovering the high-frequency edge details of the images in noise-contaminated imaging conditions, e.g., remote sensing satellite imaging. In this paper, we propose a generative adversarial network (GAN)-based edge-enhancement network (EEGAN) for robust satellite image SR reconstruction along with the adversarial learning strategy that is insensitive to noise. In particular, EEGAN consists of two main subnetworks: an ultradense subnetwork (UDSN) and an edge-enhancement subnetwork (EESN). In UDSN, a group of 2-D dense blocks is assembled for feature extraction and to obtain an intermediate high-resolution result that looks sharp but is eroded with artifacts and noises as previous GAN-based methods do. Then, EESN is constructed to extract and enhance the image contours by purifying the noise-contaminated components with mask processing. The recovered intermediate image and enhanced edges can be combined to generate the result that enjoys high credibility and clear contents. Extensive experiments on Kaggle Open Source Data set, Jilin-1 video satellite images, and Digitalglobe show superior reconstruction performance compared to the state-of-the-art SR approaches.

I. Introduction

In more recent years, satellite imagery processing has drawn widespread attention because of its special value in extensive application scenarios [1], [2] (e.g., time-span comparative studies [3], land cover classification [4]–[7], natural disaster warning [8], assessment of urban economic levels, resource exploration [9], etc.). However, because of hardware cost and craftsmanship limitations, the resolution of the observed images often fails to meet the demand, thus bringing negative impacts on the accuracy of subsequent computer vision tasks [10], [11]. Therefore, how to provide high-quality satellite imageries in a cost-effective manner is mainly discussed in this paper.