Crater detection by a boosting approach

IEEE Geoscience and Remote Sensing Letters

Volumn 6, Issue 1, 2009, Pages 127-131

  Martins, Ricardo ^a   Pina, Pedro ^a   Marques, Jorge S ^a,b   Silveira, Margarida ^a,b  

^a INSTITUTO SUPERIOR TÉCNICO   (Portugal)

^b INSTITUTE FOR SYSTEMS AND ROBOTICS   (Portugal)

Author keywords

Automatic detection; Boosting; Impact craters; Learning; Mars

Indexed keywords

MARTIAN SURFACE ANALYSIS;

AUTOMATIC DETECTION; BOOSTING; IMPACT CRATERS; LEARNING; MARS;

LEARNING ALGORITHMS;

EID: 58149488744     PISSN: 1545598X     EISSN: None     Source Type: Journal    
DOI: 10.1109/LGRS.2008.2006004     Document Type: Article

References (19)

1. W. K. Hartmann and G. Neukum, "Cratering chronology and the evolution of Mars", Space Sci. Rev., vol. 96, no. 1-4, pp. 165-194, Apr. 2001.
2. A. Flores-Méndez, Crater Marking and Classification Using Computer Vision, Lecture Notes in Computer Science, vol. 2905. Berlin, Germany: Springer-Verlag, 2003, pp. 79-86.
3. G. Michael, "Coordinate registration by automated crater recognition", Planetary Space Sci., vol. 51, no. 9, pp. 563-568, Aug. 2003.
4. T. Barata, E. I. Alves, J. Saraiva, and P. Pina, Automatic Recognition of Impact Craters on the Surface of Mars, Lecture Notes in Computer Science, vol. 3212. Berlin, Germany: Springer-Verlag, 2004, pp. 489-496.
5. L. Bandeira, J. Saraiva, and P. Pina, "Impact crater recognition on Mars based on a probability volume created by template matching", IEEE Trans. Geosci. Remote Sens., vol. 45, no. 12, pp. 4008-4015, Dec. 2007.
6. T. Vinogradova, M. Burl, and E. Mjolness, "Training of a crater detection algorithm for Mars crater imagery", in Proc. IEEE Aerosp. Conf., Big Sky, MT, 2002, vol. 7, pp. 3201-3211.
7. J. R. Kim, J.-P. Muller, S. van Gasselt, J. G. Morley, and G. Neukum, "Automated crater detection, a new tool for Mars cartography and chronology", Photogramm. Eng. Remote Sens., vol. 71, no. 10, pp. 1205-1217, Oct. 2005.
8. B. D. Bue and T. F. Stepinski, "Machine detection of Martian impact craters from digital topography data", IEEE Trans. Geosci. Remote Sens., vol. 45, no. 1, pp. 265-274, Jan. 2007.
9. A. A. Smirnov, "Exploratory study of automated crater detection algorithm", Boulder, CO, 2002. Tech. Rep.
10. P. G. Wetzler, B. Enke, W. J. Merline, C. R. Chapman, and M. C. Burl, "Learning to detect small impact craters", in Proc. 7th IEEE Workshops Appl. Comput. Vision WACV/MOTIONS, vol. 1, 2005, pp. 178-184.
11. S. Brumby, C. Plesko, and E. Asphaug, "Evolving automated feature extraction algorithms for planetary science", in Proc. ISPRS WG IV/9: Extraterrestrial Mapping Workshop Advances-Planetary Mapping, Houston, TX, 2003.
12. C. Plesko, S. Werner, S. Brumby, E. Asphaug, and G. Neukum, "A statistical analysis of automated crater counts in MOC and HRSC data", in Proc. 37th Lunar Planetary Sci., Houston, TX, 2006.
13. E. R. Urbach, "Classification of objects consisting of multiple segments with application to crater detection", in Proc. 8th ISMM, Rio de Janeiro, Brazil, 2007, vol. 2, pp. 81-82.
14. B. Leroy, G. G. Medioni, E. Johnson, and L. Matthies, "Crater detection for autonomous landing on asteroids", Image Vis. Comput., vol. 19, no. 11, pp. 787-792, Sep. 2001.
15. Y. Sawabe, T. Matsunaga, and S. Rokugawa, "Automated detection and classification of lunar craters using multiple approaches", Adv. Space Res., vol. 37, no. 1, pp. 21-27, 2006.
16. P. Viola and M. Jones, "Robust real-time face detection", Int. J. Comput. Vis., vol. 57, no. 2, pp. 137-154, May 2004.
17. C. Papageorgiou, M. Oren, and T. Poggio, "A general framework for object detection", in Proc. 6th ICCV, 1998, pp. 555-562.
18. Y. Deng and G. Su, "Face detection based on fuzzy cascade classifier with scale-invariant features", Int. J. Inf. Technol., vol. 12, no. 5, pp. 108-116, 2006.
19. G. Salamunićcar and S. Loncaric, "Open framework for objective evaluation of crater detection algorithms with first test-field subsystem based on MOLA data", Adv. Space Res., vol. 42, no. 1, pp. 6-19, 2008.