Bernoulli trials based feature selection for crater detection

Counting craters is a fundamental task of planetary science because it provides the only tool for measuring relative ages of planetary surfaces. However, advances in surveying craters present in data gathered by planetary probes have not kept up with advances in data collection. One challenge of auto-detecting craters in images is to identify an image's features that discriminate it between craters and other surface objects. The problem of optimal feature selection is known to be NP-hard and the search is computationally intractable. In this paper we propose a wrapper based randomized feature selection method to efficiently select relevant features for crater detection. We design and implement a dynamic programming algorithm to search for a relevant feature subset by removing irrelevant features and minimizing a cost objective function simultaneously. In order to only remove irrelevant features we use Bernoulli Trials to calculate the probability of such a case using the cost function. Our proposed algorithms are empirically evaluated on a large high-resolution Martian image exhibiting a heavily cratered Martian terrain characterized by heterogeneous surface morphology. The experimental results demonstrate that the proposed approach achieves a higher accuracy than other existing randomized approaches to a large extent with less runtime.