Comparison and evaluation of feature matching methods for multisource planetary remote sensing imagery

Abstract

Feature‐based image matching is a critical technique in photogrammetry and computer vision. Recently, various advanced image matching methods have been proposed. The effectiveness of these methods is significantly challenged in the case of multisource planetary images which often have to deal with unique surface morphologies, observed by different sensors and under different illumination and viewing conditions. This study investigates and evaluates the performances of 13 feature detectors across diverse images from Moon and Mars, captured by different sensor systems under different radiometric and geometric conditions. Also, the performances of 12 feature descriptors are assessed. A ranking for combinations of detectors and descriptors is determined. The results reveal that phase congruency‐based algorithms achieve favourable performance in both feature detection and description. On the other hand methods based on deep learning may obtain better results if training data of high quality were available. Finally, we summarise the capabilities and limitations of multisource remote sensing image matching methods and provide discussions and prospects for future research.