Towards an automatic counter of lunar craters

Quantification of impact craters on planetary surfaces is relevant to understand the geological history of the planet. In order to automatize quantification of lunar craters in digital images, the first step is to develop a computational tool capable of classifying a subwindow of pixels into two possible outputs: crater / non-crater. In this paper, we provide preliminary experimental results using an adaptive boosting algorithm to train a binary classifier for lunar crater identification. Using 30 weak classifiers we obtain 0.925 and 0.94 of sensitivity and specificity, respectively.