Automatic Recognition of Impact Craters on the Surface of Mars

Machine cataloging of impact craters on Mars

“…In order to achieve these goals, the AutoCrat is the first topography-based crater detection algorithm (CDA). Existing research on CDAs focuses almost exclusively on image-based algorithms (Barata et al, 2004;Burl et al, 2001;Cheng et al, 2003;Honda et al, 2002;Kim et al, 2005;Leroy et al, 2001;Plesko et al, 2004;Vinogradova et al, 2002;Wetzler et al, 2005). Image-based CDAs are quite involved because of inherent limitations of imagery data; the ''visibility" of an impact crater depends on the quality of an image, which, in turn, depends on illumination, surface properties, and atmospheric state.…”

Detecting Impact Craters in Planetary Images Using Machine Learning

“…The first class of methods rely exclusively on pattern recognition techniques to identify crater rims having circular or elliptical features in an image (for example, (Barata, Alves, Saraiva, & Pina, 2004) (Cheng, Johnson, Matthies, & Olson, 2002) (Honda, Iijima, & Konishi, 2002) (Kim, Muller, S., J., & Neukum, 2005) (Leroy, Medioni, & Matthies, 2001) (Salamaniccar & Loncaric, 2010) . The general idea of such methods is to first preprocess an image to enhance the edges of the crater rims, and then to detect the craters using variants of the Hough Transform (Hough V, 1962), genetic algorithms (Honda, Iijima, & Konishi, 2002), or the radial consistency algorithm (Earl, Chicarro, Koeberl, Marchetti, & Milnes, 2005) that identifies regions of rotational symmetry.…”

Effective classification for crater detection: A case study on Mars

“…developed a validation process applied to a feature-identification algorithm which is a typical template matching method [4]. Texture analysis is applied to crater detection in [5], which segments the images through the principal component analysis (PCA) of statistical texture measures. Honda et.…”