  Feature matching determines the orientation accuracy for the High Spatial
Resolution (HSR) optical satellite stereos, subsequently impacting several
significant applications such as 3D reconstruction and change detection.
However, the matching of off-track HSR optical satellite stereos often
encounters challenging conditions including wide-baseline observation,
significant radiometric differences, multi-temporal changes, varying spatial
resolutions, inconsistent spectral resolution, and diverse sensors. In this
study, we evaluate various advanced feature matching algorithms for HSR optical
satellite stereos. Utilizing a specially constructed dataset from five
satellites across six challenging scenarios, HSROSS Dataset, we conduct a
comparative analysis of four algorithms: the traditional SIFT, and
deep-learning based methods including SuperPoint + SuperGlue, SuperPoint +
LightGlue, and LoFTR. Our findings highlight overall superior performance of
SuperPoint + LightGlue in balancing robustness, accuracy, distribution, and
efficiency, showcasing its potential in complex HSR optical satellite
scenarios.
