Crater detection based on local non-negative matrix factorization

Due to the variations in the terrain, illumination and scale, it is difficult to detect craters from remote sensing image of planet surface. This paper proposes a novel automatic crater detection method by introducing the local non-negative matrix factorization (LNMF) for remote sensing images of Martian surface. LNMF is aimed at learning localized, part-based features from global samples, which has shown considerable prospect in feature extraction. Our detection algorithm contains three key procedures. Firstly, the crater candidates are detected by geometry approaches. Secondly, LNMF is applied in subspace learning for all crater samples and candidates. At last, we get the final detection results by discarding non-craters in candidates. The LNMF-based method has achieved satisfied results in the experiments conducted on the Mars Orbiter Camera (MOC) dataset.