Craters detection via possibilistic shell clustering
Mauro Barni, Alessandro Mecocci, Luca Perugini · 2000
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.