An Automatic Evaluation Platform for Feature Matching Algorithms Based on an Orbital Optical Pushbroom Stereo Imaging System

Thanks to the high robustness of feature-based matching algorithms, they can be applied in remote sensing (RS) applications with complex image changes. However, evaluating feature matching algorithms on RS images is still challenging, because creating ground truth matching data sets of RS images costs a lot of both computing time and human operator time. Therefore, in this letter, we present an evaluation platform for simulating RS ground truth data sets of feature-points correspondences based on a customizable orbital optical pushbroom stereo imaging system. With the help of the proposed platform, evaluating feature matching algorithms could be fully automatic and customized. The performance of three state-of-the-art feature matching algorithms based on local transformation constraint is evaluated and discussed on the proposed platform with comprehensive experiments. The evaluation results of the platform are also compared with the manual evaluation results of 10 pairs of real RS stereo images. The evaluation results show that the proposed platform indeed offers an efficient way for evaluating RS feature matching algorithms.