  Deep learning (DL) stereo matching methods gained great attention in remote
sensing satellite datasets. However, most of these existing studies conclude
assessments based only on a few/single stereo images lacking a systematic
evaluation on how robust DL methods are on satellite stereo images with varying
radiometric and geometric configurations. This paper provides an evaluation of
four DL stereo matching methods through hundreds of multi-date multi-site
satellite stereo pairs with varying geometric configurations, against the
traditional well-practiced Census-SGM (Semi-global matching), to
comprehensively understand their accuracy, robustness, generalization
capabilities, and their practical potential. The DL methods include a
learning-based cost metric through convolutional neural networks (MC-CNN)
followed by SGM, and three end-to-end (E2E) learning models using Geometry and
Context Network (GCNet), Pyramid Stereo Matching Network (PSMNet), and
LEAStereo. Our experiments show that E2E algorithms can achieve upper limits of
geometric accuracies, while may not generalize well for unseen data. The
learning-based cost metric and Census-SGM are rather robust and can
consistently achieve acceptable results. All DL algorithms are robust to
geometric configurations of stereo pairs and are less sensitive in comparison
to the Census-SGM, while learning-based cost metrics can generalize on
satellite images when trained on different datasets (airborne or ground-view).
