[IPOL discuss] [IPOL announce] new article: Disparity Estimation Networks for Aerial and High-Resolution Satellite Images: A Review

[announcements about the IPOL journal]

A new article is available in IPOL: https://www.ipol.im/pub/art/2022/435/

Roger Marí, Thibaud Ehret, and Gabriele Facciolo,
Disparity Estimation Networks for Aerial and High-Resolution Satellite 
Images: A Review,
Image Processing On Line, 12 (2022), pp. 501–526.
https://doi.org/10.5201/ipol.2022.435

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.




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