Disparity Estimation Networks for Aerial and High-Resolution Satellite Images: A Review

Abstract

This study reviews the PSM and HSM deep learning architectures for disparity estimation from an input stereo pair and assesses their applicability for satellite stereo reconstruction. All methods are tested on urban landscapes unseen at training time, using pre-trained weights learned from a stereo matching benchmark for aerial imagery. The quality of the disparity maps output by each method is assessed based on the subsequent surface models, which are evaluated using a lidar reference model. The conducted experiments give insight into the robustness of each architecture (e.g. robustness to different input resolutions, color spaces or acquisition dates), as well as their generalizability across different cities. Lastly, the results obtained with the different networks are compared with those of a state-of-the-art variant of the semi-global matching algorithm, which is a well-known classic methodology for satellite dense stereo matching. **This is an MLBriefs article, the source code has not been reviewed!**<br> **The original source codes are available [[here|https://github.com/JiaRenChang/PSMNet]] and [[here|https://github.com/gengshan-y/high-res-stereo]] (last checked 2022/11/14).**<br> <span style="color:red">**BEST STUDENT PAPER MLBRIEFS 2022**</span>.