A comparative study on deep-learning methods for dense image matching of multi-angle and multi-date remote sensing stereo-images

Deep-learning (DL) stereomatching 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 stereomatching methods through hundreds of multi-date multi-site satellite stereopairs with varying geometric configurations, against the traditional well-practiced Census-semi-global matching (SGM), to comprehensively understand their accuracy, robustness, generalisation 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 generalise 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 stereopairs and are less sensitive in comparison to the Census-SGM, while learning-based cost metrics can generalise on satellite images when trained on different datasets (airborne or ground-view).

Automated summary

This paper evaluates four DL stereomatching methods using a large number of satellite stereopairs with varying geometric configurations. The experiments show that E2E algorithms can achieve high geometric accuracies, but may not generalize well for unseen data. On the other hand, the learning-based cost metric and Census-SGM are robust and consistently achieve acceptable results. The DL algorithms are robust to geometric configurations of stereopairs and have the potential to generalize on satellite images trained on different datasets.