FROM PLANETSCOPE TO WORLDVIEW: MICRO-SATELLITE IMAGE SUPER-RESOLUTION WITH OPTIMAL TRANSPORT DISTANCE

The vast majority of prior work on satellite image super-resolution (SR) assumes the availability of paired training data, and then uses low-resolution (LR) images artificially generated by simple blurring and/or down-sampling. These methods often fail to produce convincing results in real-world data since the actual degradation is much more complex than manually designed. This paper presents a deep learning framework to model the degradation process of micro-satellite image. To this end, we first introduce remote sensing dataset consisting of WorldView (0.4m) and PlanetScope (3m) satellite images. They are aligned to the same coordinate, but are collected at different days/times. Using such data, we design Degradation Network (DegNet), generating realistic micro-satellite images from its high-resolution (HR) counterpart. A degradation loss using an optimal transport distance is proposed which makes the empirical distribution, i.e., histogram of outputs to be similar to that of real micro-satellite images. It faithfully reflects the degradation characteristic of micro-satellite while preserving the content of an input. Finally, a SR network is trained with the generated LR-HR pairs. Extensive experiments show that the proposed method greatly improves the SR performance on real-world data.