Title :
Craters detection via possibilistic shell clustering
Author :
Barni, M. ; Mecocci, A. ; Perugini, L.
Author_Institution :
Dept. of Inf. Eng., Siena Univ., Italy
Abstract :
A new circle extraction algorithm based on possibilistic clustering is presented along with its application to automatic crater detection in remote sensing images. With respect to classical algorithms based on fuzzy shell-clustering, solutions are proposed to make circle extraction robust against noise and non-circular structures. The proposed algorithm operates by grouping edge pixels into connected subgroups, and by fitting a circle to each group through possibilistic clustering. Circles are refined through PCS clustering, and validated by means of geometrical considerations. The effectiveness of the proposed approach for crater detection is confirmed by experimental results.
Keywords :
edge detection; feature extraction; fuzzy set theory; geophysical signal processing; meteorite craters; pattern clustering; terrain mapping; PCS clustering; automatic crater detection; circle extraction algorithm; connected subgroups; edge pixels; geometrical considerations; possibilistic clustering; possibilistic shell clustering; remote sensing images; Clustering algorithms; Clustering methods; Data mining; Detection algorithms; Image edge detection; Noise robustness; Personal communication networks; Prototypes; Remote sensing; Satellites;
Conference_Titel :
Image Processing, 2000. Proceedings. 2000 International Conference on
Conference_Location :
Vancouver, BC, Canada
Print_ISBN :
0-7803-6297-7
DOI :
10.1109/ICIP.2000.899810