Crater detection and classification are critical elements for planetary mission preparations and landing site selection. This
paper presents a methodology for the automated detection and matching of craters on images of planetary surface such as
Moon, Mars and asteroids. For craters usually are bowl shaped depression, craters can be figured as circles or circular arc
during landing phase. Based on the hypothesis that detected crater edges is related to craters in a template by translation,
rotation and scaling, the proposed matching method use circles to fitting craters edge, and align circular arc edges from
the image of the target body with circular features contained in a model. The approach includes edge detection, edge
grouping, reference point detection and geometric circle model matching. Finally we simulate planetary surface to test
the reasonableness and effectiveness of the proposed method.