An experimental comparison of multi-view stereo approaches on satellite images

Abstract

Different methods can be applied to satellite images to derive an altitude map from a set of images. In this article we evaluate a set of representative methods from different approaches. We consider true multi-view stereo methods as well as pair-wise ones, classic methods and deep learning based ones, methods already in use on satellite images and others that were originally devised for close range imaging and are adapted to satellite imagery. While deep learning (DL) methods have taken over multi-view stereo reconstruction in the last years, this tendency has not fully reached satellite stereo pipelines that still largely rely on pair-wise classic algorithms. For the comparison, we set-up a framework that allows to interface a DL-based stereo method taken from the computer vision literature with a satellite stereo pipeline. For multi-view stereo algorithms we build on a recently proposed framework originally devised to apply Colmap method to satellite images. Methods are compared on several datasets that include sets of images taken within a few days and sets of images taken months apart. Results show that DL methods have, in general, a good generalization power. In particular, the use of the GANet DL method as the matching step in a pair-wise stereo pipeline is promising as it already performs better than the classic counterpart, even without a specific training.