Using greyscale texture features recently become a new trend in supervised machine learning crater detection. Need to be analysed image data preferably by automatic technique as the data is in huge amount. Automatic feature extraction method is proposed and utilised for earth remote sensing images. These are not always applicable to planetary data which is having low contrast and uneven illumination features. Proposed a new method which is unsupervised for different ellipsoidal feature extraction for planetary image registration. Approach is based on combination of various techniques including Hough Transform and watershed segmentation technique. This is mainly applicable for geometrically compact shapes rocks, craters, and other geological features

1. J. Le Moigne, N. S. Netanyahu, and R. D. Eastman, Image Registration for Remote Sensing. Cambridge, U. K. : Cambridge Univ. Press, 2011.
2. A. Flores-Mendez, Crater Marking and Classification Using ComputerVision. New York: Springer-Verlag, 2003.
3. G. Salamuniccar and S. Loncaric, "Method for crater detection from Martian digital topography data using gradient value/orientation, morphometry,vote analysis, slip tuning, and calibration," IEEE Trans. Geosci. Remote Sens. , vol. 48, no. 5, pp. 2317–2329, May 2010.
4. T. Barata, E. I. Alves, J. Saraiva, and P. Pina, Automatic Recognition of Impact Craters on the Surface of Mars. Berlin, Germany: Springer- Verlag, 2004.
5. J. R. Kim and J. -P. Muller, "Impact crater detection on optical images and DEMs," in Proc. ISPRS, Houston, TX, 2003.
6. A. A. Smirnov, "Exploratory study of automated crater detection algorithm," Dept. Comput. Sci. , Univ. Colorado, Boulder, CO, Tech. Rep. , 2002.
7. R. Martins, P. Pina, J. S. Marques, and M. Silveira, "A boosting algorithm for crater detection," in Proc. Vis. , Imaging, Image Process. Conf. , Palma de Mallorca, Spain, 2008, pp. 197–201.
8. 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.
9. 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.
10. 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.
11. R. Martins, P. Pina, J. S. Marques, and M. Silveira, "Crater detection by a boosting approach," IEEE Geosci. Remote Sens. Lett. , vol. 6, no. 1,pp. 127–131, Jan. 2009.
12. P. Viola and M. Jones, "Robust real-time face detection," Int. J. Comput. Vis. , vol. 57, no. 2, pp. 137–154, May 2004.
13. E. R. Urbach and T. F. Stepinski, "Automatic detection of sub-km craters in high resolution planetary images," Planet. Space Sci. , vol. 57, no. 7, pp. 880–887, Jun. 2009.
14. D. Thompson, S. Niekum, T. Smith, and D. Wettergreen, "Automatic detection and classification of features of geologic interest," in Proc. IEEE Aerosp. Conf. , 2005, pp. 366–377.
15. D. Thompson and R. Castano, "Performance comparison of rock detection algorithms for autonomous planetary geology," in Proc. IEEE Aerosp. Conf. , 2007, pp. 1–9.
16. H. Dunlop, D. Thompson, and D. Wettergreen, "Multi-scale features for detection and segmentation of rocks in Mars images," in Proc. IEEE Conf. Comput. Vis. Pattern Recog. , 2007, pp. 1–7.
17. M. P. Golombek, R. E. Arvidson, T. Heet, L. Barry, J. R. Matijevic, and A. S. McEwen, "Size-frequency distributions of rocks on the northern plains of Mars in HiRISE images with special reference to Phoenix landing sites," in Proc. LPSC, League City, TX, 2007, vol. 38, p. 1405.
18. J. R. Kim, J. -P. Muller, J. G. Morley, and K. L. Mitchell, "Automated registration of MDIM with MOLA tracks," in Proc. Annu. Lunar Planet. Sci. Conf. , Houston, TX, 2001, vol. 32, p. 2087.
19. G. G. Michael, "Coordinate registration by automated crater recognition," Planet. Space Sci. , vol. 51, no. 9/10, pp. 563–568, Aug. /Sep. 2003.
20. S. -Y. Lin, J. -P. Muller, J. P. Mills, and P. E. Miller, "An assessment of surface matching for the automated co-registration of MOLA, HRSC and HiRISE DTMs," Earth Planet. Sci. Lett. , vol. 294, no. 3/4, pp. 520–533, Jun. 2010.
21. L. Vincent and P. Soille, "Watersheds in digital spaces: An efficient algorithm based on immersion simulations," IEEE Trans. Pattern Anal. Mach. Intell. , vol. 13, no. 6, pp. 583–598, Jun. 1991.
22. J. Canny, "A computational approach to edge detection," IEEE Trans. Pattern Anal. Mach. Intell. , vol. PAMI-8, no. 6, pp. 679–698, Nov. 1986.
23. S. Tsuji and F. Matsumoto, "Detection of ellipses by a modified Hough transformation," IEEE Trans. Comput. , vol. C-27, no. 8, pp. 777–781, Aug. 1978.
24. G. Salamuniccar and S. Loncaric, "GT-57633 catalogue of Martian impact craters developed for evaluation of crater detection algorithms," Planet. Space Sci. , vol. 56, no. 15, pp. 1992–2008, Dec. 2008

Hough Transform Watershed Segmentation Feature Extraction Crater Detection.