Parallax estimation for push-frame satellite imagery: application to super-resolution and 3D surface modeling from Skysat products
This work is significant for remote sensing practitioners and researchers working with multi-frame satellite imagery, as it improves the accuracy of super-resolution and enables 3D surface modeling by accounting for parallax.
This paper addresses the issue of parallax in push-frame satellite imagery, specifically from Skysat, which hinders multi-frame super-resolution. They propose a parallax estimation method that decomposes apparent motion into Plane+Parallax and uses a multi-frame optical flow algorithm, demonstrating its importance for super-resolution and coarse 3D surface modeling in scenes with elevation changes.
Recent constellations of satellites, including the Skysat constellation, are able to acquire bursts of images. This new acquisition mode allows for modern image restoration techniques, including multi-frame super-resolution. As the satellite moves during the acquisition of the burst, elevation changes in the scene translate into noticeable parallax. This parallax hinders the results of the restoration. To cope with this issue, we propose a novel parallax estimation method. The method is composed of a linear Plane+Parallax decomposition of the apparent motion and a multi-frame optical flow algorithm that exploits all frames simultaneously. Using SkySat L1A images, we show that the estimated per-pixel displacements are important for applying multi-frame super-resolution on scenes containing elevation changes and that can also be used to estimate a coarse 3D surface model.