A Novel Approach for Multiscale Lunar Crater Detection by the Use of Path-Profile and Isolation Forest Based on High-Resolution Planetary Images
Author:
Affiliation:
1. Shanghai Key Laboratory of Space Mapping and Remote Sensing for Planetary Exploration, College of Surveying and Geo-Informatics, Tongji University, Shanghai, China
2. Department of Surveying and Geo-Informatics, Tongji University, Shanghai, China
Funder
National Natural Science Foundation of China
Dawn Scholar of Shanghai Program
Shanghai Rising-Star Program
State Key Laboratory of Disaster Reduction in Civil Engineering
Fundamental Research Funds for the Central Universities of China
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
General Earth and Planetary Sciences,Electrical and Electronic Engineering
Link
http://xplorestaging.ieee.org/ielx7/36/9633014/09718579.pdf?arnumber=9718579
Reference54 articles.
1. Mapping the Topographic Features of Mining-Related Valley Fills Using Mask R-CNN Deep Learning and Digital Elevation Data
2. Mask R-CNN
3. Coarse-to-Fine Extraction of Small-Scale Lunar Impact Craters From the CCD Images of the Chang’E Lunar Orbiters
4. Detection of sub-kilometer craters in high resolution planetary images using shape and texture features
5. Automated crater detection and counting using the hough transform
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