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http://dx.doi.org/10.12652/Ksce.2018.38.6.0859

A Deep-Learning Based Automatic Detection of Craters on Lunar Surface for Lunar Construction  

Shin, Hyu Soung (Korea Institute of Civil Engineering and Building Technology)
Hong, Sung Chul (Korea Institute of Civil Engineering and Building Technology)
Publication Information
KSCE Journal of Civil and Environmental Engineering Research / v.38, no.6, 2018 , pp. 859-865 More about this Journal
Abstract
A construction of infrastructures and base station on the moon could be undertaken by linking with the regions where construction materials and energy could be supplied on site. It is necessary to detect craters on the lunar surface and gather their topological information in advance, which forms permanent shaded regions (PSR) in which rich ice deposits might be available. In this study, an effective method for automatic detection of lunar craters on the moon surface is taken into consideration by employing a latest version of deep-learning algorithm. A training of a deep-learning algorithm is performed by involving the still images of 90000 taken from the LRO orbiter on operation by NASA and the label data involving position and size of partly craters shown in each image. the Faster RCNN algorithm, which is a latest version of deep-learning algorithms, is applied for a deep-learning training. The trained deep-learning code was used for automatic detection of craters which had not been trained. As results, it is shown that a lot of erroneous information for crater's positions and sizes labelled by NASA has been automatically revised and many other craters not labelled has been detected. Therefore, it could be possible to automatically produce regional maps of crater density and topological information on the moon which could be changed through time and should be highly valuable in engineering consideration for lunar construction.
Keywords
Deep-learning algorithm; Lunar construction; Lunar craters; Optimum landing sites on the moon;
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Times Cited By KSCI : 1  (Citation Analysis)
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1 Bandeira, L., Ding, W. and Stepinski, T. F. (2012). "Detection of subkilometer craters in high resolution planetary images using shape and texture features." Advances in Space Research, Vol. 49, pp. 64-74.   DOI
2 Chen, M., Liu, D., Qian, K., Li, J., Lei, M. and Zhou, Y. (2018). "Lunar crater detection based on terrain analysis and mathematical morphology methods using digital elevation models." IEEE Transactions on Geoscience and Remote Sensing, Vol. 56, No. 7, pp. 3681-3692.   DOI
3 Cohen, J. P., Lo, H. Z., Lu, T. and Ding, W. (2016). "Crater detection via convolutional neural networks." Proc. of 47th Lunar and Planetary Science Conference, Houston, U.S.A., p. 1143.
4 Emami, E., Bebis, G., Nefian, A. and Fong, T. (2015). "Automatic crater detection using convex grouping and convolutional neural networks." Proc. of International Symposium on Visual Computing, Springer, Cham, pp. 213-224.
5 Girshick, R. (2015). "Fast R-CNN." Proc. of the IEEE International Conference on Computer Vision, Las Condes, Chile, pp. 1440-1448.
6 Hong, S., Kim, Y., Seo, M. and Shin, H. (2018) "Geographic distribution analysis of lunar in-situ resource and topography to construct lunar base." Journal of the Korea Academia-Industrial Cooperation Society, Vol. 19, pp. 669-676 (in Korean).
7 Palafox, L. F., Hamilton, C. W., Scheidt, S. P. and Alvarez, A. M. (2017). "Automated detection of geological landforms on mars using convolutional neural networks." Computers & Geosciences, Vol. 101, pp. 48-56.   DOI
8 Ren, S., He, K., Girshick, R. and Sun, J. (2015), "Faster R-CNN: towards real-time object detection with region proposal networks." Advances in neural information processing systems, pp. 91-99.
9 Robbins, S. J., Antonenko, I., Kirchoff, M. R., Chapman, C. R., Fassett, C. I., Herrick, R. R., Singer, K., Zanetti, M., Lehan, C., Huang, D. and Gay, P. L. (2014). "The variability of crater identification among expert and community crater analysts." Icarus 234, pp. 109-131.   DOI
10 Salamuniccar, G. and Loncaric, S. (2010). "Method for crater detection from digital topography data: interpolation based improvement and application to Lunar SELENE LALT data." Proc. of 38th COSPAR Scientific Assembly, Vol. 38, p. 3.
11 Silburt, A., Ali-Dib, .M, Zhu, C., Jackson, A., Menou, K. (2018) DeepMoon Supplemental Materials. Available at https://zenodo.org/record/1133969#.W_47ODMUmHs (Accessed: March 23, 2018)
12 Stepinski, T., Ding, W. and Vilalta, R. (2012). "Detecting impact craters in planetary images using machine learning." Intelligent data analysis for real-life applications: theory and practice, IGI Global, pp. 146-159
13 Wetzler, P., Honda, R., Enke, B., Merline, W., Chapman, C. and Burl, M. (2005). "Learning to detect small impact craters." Proc. of 7th IEEE Workshop on Application of Computer Vision, Vol. 1. pp. 178-184.
14 Zhu, M. (2004), "Recall, precision and average precision." Department of Statistics and Actuarial Science, University of Waterloo, Vol. 2, p. 30.
15 Kim, K. J. (2017). "A research trend on lunar resource and lunar base." The Journal of The Petrological Society of Korea, Vol. 26, No. 4, pp. 373-384 (In Korean).   DOI
16 NASA (2018). Lunar Reconnaissance Orbiter., Available at: https://lunar.gsfc.nasa.gov (Accessed: October 12, 2018)