Application of possibilistic shell-clustering to the detection of craters in real-world imagery

Automatic craters detection in remote sensing images is addressed by means of clustering-based circle detection. 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 craters detection is confirmed by experimental results.