Automated crater detection from co-registered optical images, elevation maps and slope maps using deep learning

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dc.contributor.author Tewari, Atal
dc.contributor.author Verma, Vinay
dc.contributor.author Srivastava, Pradeep
dc.contributor.author Jain, Vikrant
dc.contributor.author Khanna, Nitin
dc.date.accessioned 2021-01-15T13:10:01Z
dc.date.available 2021-01-15T13:10:01Z
dc.date.issued 2020-12
dc.identifier.citation Tewari, Atal; Verma, Vinay; Srivastava, Pradeep; Jain, Vikrant and Khanna, Nitin, "Automated crater detection from co-registered optical images, elevation maps and slope maps using deep learning", arXiv, Cornell University Library, DOI: arXiv:2012.15281, Dec. 2020. en_US
dc.identifier.uri http://arxiv.org/abs/2012.15281
dc.identifier.uri https://repository.iitgn.ac.in/handle/123456789/6215
dc.description.abstract Impact craters are formed as a result of continuous impacts on the surface of planetary bodies. This paper proposes a novel way of simultaneously utilizing optical images, digital elevation maps (DEMs), and slope maps for automatic crater detection on the lunar surface. Mask R-CNN, tuned for the crater detection task, is utilized in this paper. Two catalogs, namely, Head-LROC and Robbins, are used for the performance evaluation. Exhaustive analysis of the detection results on the lunar surface has been performed with respect to both Head-LROC and Robbins catalog. With the Head-LROC catalog, which has relatively strict crater markings and larger possibility of missing craters, recall value of 94.28\% has been obtained as compared to 88.03\% for the baseline method. However, with respect to a manually marked exhaustive crater catalog based on relatively liberal marking, significant precision and recall values are obtained for different crater size ranges. The generalization capability of the proposed method in terms of crater detection on a different terrain with different input data type is also evaluated. We show that the proposed model trained on the lunar surface with optical images, DEMs and corresponding slope maps can be used to detect craters on the Martian surface even with entirely different input data type, such as thermal IR images from the Martian surface.
dc.description.statementofresponsibility by Atal Tewari, Vinay Verma, Pradeep Srivastava, Vikrant Jain and Nitin Khanna
dc.language.iso en_US en_US
dc.publisher Cornell University Library en_US
dc.subject Electrical Engineering and Systems Science en_US
dc.subject Image and Video Processing en_US
dc.title Automated crater detection from co-registered optical images, elevation maps and slope maps using deep learning en_US
dc.type Pre-Print en_US
dc.relation.journal arXiv


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