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 attributes that discriminate it between craters and other surface objects. The problem of selecting the optimal subset of these attributes is known to be NP-hard and the search is computationally intractable. 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 approaches.
Crater Detection via Genetic Search Methods to Reduce Image Features
Adaptive Selective Learning for Automatic Identification of Sub-Kilometer Craters
Bernoulli Trials Based Feature Selection for Crater Detection
Detection of Sub-Kilometer Craters in High Resolution Planetary Images Using Shape and Texture Features
Genetically Enhanced Feature Selection of Discriminative Planetary Crater Image Features
Machine Learning Approaches to Detecting Impact Craters in Planetary Images
Semi-Supervised Active Class Selection for Automatic Identification of Sub-Kilometer Craters
Crater Detection Using Bayesian Classifiers and LASSO
Biologically Inspired Model for Crater Detection
Sub-Kilometer Crater Discovery with Boosting and Transfer Learning
Automatic Detection of Craters in Planetary Images: An Embedded Framework Using Feature Selection and Boosting
W. Ding, T. Stepinski, L. Bandeira*, R. Vilalta, Y. Wu*, Z. Lu*, T. Cao*, in Proc. of the 19th ACM International Conference on Information and Knowledge Management (CIKM 2010), Toronto, Canada, October, 2010
Effective Classification for Crater Detection: A Case Study on Mars
J. Wang*, W. Ding, B. Fradkin*, C. H. Pham*, P. Sherman*, B. D. Tran*, D. Wang*, Y. Yang* and T. F. Stepinski, in Proc. of the 9th IEEE International Conference on Cognitive Informatics, Beijing, China, July, 2010.
Joseph Paul Cohen, Siyi Liu, Wei Ding, Yang Mu, T. F. Stepinski, Y. Wang, J. Wang