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Remote Sensing
Volume 13
Issue 9
10.3390/rs13091826
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Article

Detection of Small Impact Craters via Semantic Segmenting Lunar Point Clouds Using Deep Learning Network

by
Yifan Hu
,
Jun Xiao
,
Lupeng Liu
*,
Long Zhang
and
Ying Wang
School of Artificial Intelligence, University of Chinese Academy of Sciences, No. 19 Yuquan Road, Shijingshan District, Beijing 100049, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2021, 13(9), 1826; https://doi.org/10.3390/rs13091826
Submission received: 11 April 2021 / Revised: 30 April 2021 / Accepted: 5 May 2021 / Published: 7 May 2021
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Abstract

Impact craters refer to the most salient features on the moon surface. They are of huge significance for analyzing the moon topography, selecting the lunar landing site and other lunar exploration missions, etc. However, existing methods of impact crater detection have been largely implemented on the optical image data, thereby causing them to be sensitive to the sunlight. Thus, these methods can easily achieve unsatisfactory detection results. In this study, an original two-stage small crater detection method is proposed, which is sufficiently effective in addressing the sunlight effects. At the first stage of the proposed method, a semantic segmentation is conducted to detect small impact craters by fully exploiting the elevation information in the digital elevation map (DEM) data. Subsequently, at the second stage, the detection accuracy is improved under the special post-processing. As opposed to other methods based on DEM images, the proposed method, respectively, increases the new crusher percentage, recall and crusher level F1 by 4.89%, 5.42% and 0.67%.
Keywords: small lunar impact craters; impact craters detection; deep learning method; crater detection algorithm; moon point cloud dataset small lunar impact craters; impact craters detection; deep learning method; crater detection algorithm; moon point cloud dataset
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MDPI and ACS Style

Hu, Y.; Xiao, J.; Liu, L.; Zhang, L.; Wang, Y. Detection of Small Impact Craters via Semantic Segmenting Lunar Point Clouds Using Deep Learning Network. Remote Sens. 2021, 13, 1826. https://doi.org/10.3390/rs13091826

AMA Style

Hu Y, Xiao J, Liu L, Zhang L, Wang Y. Detection of Small Impact Craters via Semantic Segmenting Lunar Point Clouds Using Deep Learning Network. Remote Sensing. 2021; 13(9):1826. https://doi.org/10.3390/rs13091826

Chicago/Turabian Style

Hu, Yifan, Jun Xiao, Lupeng Liu, Long Zhang, and Ying Wang. 2021. "Detection of Small Impact Craters via Semantic Segmenting Lunar Point Clouds Using Deep Learning Network" Remote Sensing 13, no. 9: 1826. https://doi.org/10.3390/rs13091826

APA Style

Hu, Y., Xiao, J., Liu, L., Zhang, L., & Wang, Y. (2021). Detection of Small Impact Craters via Semantic Segmenting Lunar Point Clouds Using Deep Learning Network. Remote Sensing, 13(9), 1826. https://doi.org/10.3390/rs13091826

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