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