An approach of crater automatic recognition based on contour digital elevation model from Chang’E Missions

Abstract:

In order to provide fundamental information for exploration and related scientific research on the Moon and other planets, we propose a new automatic method to recognize craters on the lunar surface based on contour data extracted from a digital elevation model (DEM). First, we mapped 16-bits DEM to 256 gray scales for data compression, then for the purposes of better visualization, the grayscale is converted into RGB image. After that, a median filter is applied twice to DEM for data optimization, which produced smooth, continuous outlines for subsequent construction of contour plane. Considering the fact that the morphology of crater on contour plane can be approximately expressed as an ellipse or circle, we extract the outer boundaries of contour plane with the same color(gray value) as targets for further identification though a 8- neighborhood counterclockwise searching method. Then, A library of training samples is constructed based on above targets calculated from some sample DEM data, from which real crater targets are labeled as positive samples manually, and non-crater objects are labeled as negative ones. Some morphological feathers are calculated for all these samples, which are major axis (L), circumference(C), area inside the boundary(S), and radius of the largest inscribed circle(R). We use R/L, R/S, C/L, C/S, R/C, S/L as the key factors for identifying craters, and apply Fisher discrimination method on the sample library to calculate the weight of each factor and determine the discrimination formula, which is then applied to DEM data for identifying lunar craters. The method has been tested and verified with DEM data from CE-1 and CE-2, showing strong recognition ability and robustness and is applicable for the recognition of craters with various diameters and significant morphological differences, making fast and accurate automatic crater recognition possible.