TY - GEN
T1 - Crater detection based on local non-negative matrix factorization
AU - Li, Hui
AU - Yin, Jihao
AU - Gu, Zetong
N1 - Publisher Copyright:
© 2014 IEEE.
PY - 2014/11/4
Y1 - 2014/11/4
N2 - 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.
AB - 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.
KW - LNMF
KW - crater
KW - detection
UR - https://www.scopus.com/pages/publications/84911443705
U2 - 10.1109/IGARSS.2014.6946474
DO - 10.1109/IGARSS.2014.6946474
M3 - 会议稿件
AN - SCOPUS:84911443705
T3 - International Geoscience and Remote Sensing Symposium (IGARSS)
SP - 521
EP - 524
BT - International Geoscience and Remote Sensing Symposium (IGARSS)
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - Joint 2014 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2014 and the 35th Canadian Symposium on Remote Sensing, CSRS 2014
Y2 - 13 July 2014 through 18 July 2014
ER -