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2018 Vol.38, Issue 6 Preview Page
December 2018. pp. 859-865
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.
달 지상 인프라 및 기지 건설은 건설재료나 에너지 확보가 가능한 지역과 연계되어야 하며, 얼음 등의 핵심 자원이 풍부한 영구음영 지역을 형성하는 달 크레이터 지형의 탐지와 정보 수집이 선행되어야 한다. 본 연구에서는 이러한 달 크레이터(crater) 객체 정보를 최신 딥러닝 알고리즘을 이용해 효과적으로 자동 탐지하는 방안에 대해 고찰하였다. 딥러닝 학습을 위해 NASA LRO 달 궤도선의 레이저 고도계 데이터를 기반으로 구축된 9만개의 수치표고모델과 개별 수치표고모델에 존재하는 크레이터들의 위치와 크기를 레이블링한 자료를 활용하였다. 딥러닝 학습은 최신 알고리즘인 Faster RCNN (Regional Convolution Neural Network)을 자체적으로 코드화하여 적용하였다. 이를 통해 학습된 딥러닝 시스템은 학습되지 않은 달표면 이미지 내 크레이터를 자동 인식하는데 적용되었으며, NASA에서 인력에 의해 정의한 크레이터 정보들의 오류를 자동 보정 가능하고, 정의되지 않은 많은 크레이터 까지도 자동 인식 가능함을 보였다. 이를 통해 공학적으로 매우 가치가 있는 각 지역별 크레이터들의 크기 분포 특성 및 발생 빈도 분석 등이 가능하게 되었으며, 향후에는 시간 이력별 변화추이도 분석 가능할 것으로 판단된다.
References
  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.
  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.
  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. 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).
  8. 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.
  9. 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.
  10. 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.
  11. 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.
  12. 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)
  13. 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
  14. 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.
  15. Zhu, M. (2004), “Recall, precision and average precision.” Department of Statistics and Actuarial Science, University of Waterloo, Vol. 2, p. 30.
  16. NASA (2018). Lunar Reconnaissance Orbiter., Available at: https://lunar.gsfc.nasa.gov (Accessed: October 12, 2018)
Information
  • Publisher :Korean Society of Civil Engineers
  • Publisher(Ko) :대한토목학회
  • Journal Title :JOURNAL OF THE KOREAN SOCIETY OF CIVIL ENGINEERS
  • Journal Title(Ko) :대한토목학회 논문집
  • Volume : 38
  • No :6
  • Pages :859-865