Bernoulli trials based feature selection for crater detection
Liu, Siyi and Cohen, Joseph Paul and Ding, Wei and Simovici, Dan and Stepinski, Tomasz

Type: Paper

  title={Bernoulli trials based feature selection for crater detection},
  author={Liu, Siyi and Ding, Wei and Cohen, Joseph Paul and Simovici, Dan and Stepinski, Tomasz},
  booktitle={Proceedings of the 19th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems},
	abstract = {Counting craters is a fundamental task of planetary sci-ence because it provides the only tool for measuring relativeages of planetary surfaces. However, advances in surveyingcraters present in data gathered by planetary probes havenot kept up with advances in data collection. One chal-lenge of auto-detecting craters in images is to identify an images features that discriminate it between craters andother surface objects. The problem of optimal feature se-lection is known to be NP-hard and the search is compu-tationally intractable. In this paper we propose a wrapperbased randomized feature selection method to efficiently se-lect relevant features for crater detection. We design andimplement a dynamic programming algorithm to search fora relevant feature subset by removing irrelevant features andminimizing a cost objective function simultaneously. In or-der to only remove irrelevant features we use Bernoulli Tri-als to calculate the probability of such a case using the costfunction. Our proposed algorithms are empirically evaluatedon a large high-resolution Martian image exhibiting a heav-ily cratered Martian terrain characterized by heterogeneoussurface morphology. The experimental results demonstratethat the proposed approach achieves a higher accuracy thanother existing randomized approaches to a large extent withless runtime.}
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