Knowledge Discovery Lab

Automatic Detection of Craters

Abstract

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.

Publications

Crater Detection via Genetic Search Methods to Reduce Image Features

J. Cohen*, W. Ding, Advances in Space Research, 2013
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Adaptive Selective Learning for Automatic Identification of Sub-Kilometer Craters

S. Liu*, W. Ding, F. Gao, T. Stepinski, Neurocomputing, 2012
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Bernoulli Trials Based Feature Selection for Crater Detection

S. Liu*, W. Ding, J. P. Cohen*, D. Simovici, T. Stepinski, the 19th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, Chicago, IL, November, 2011
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Detection of Sub-Kilometer Craters in High Resolution Planetary Images Using Shape and Texture Features

S. Liu*, W. Ding, J. P. Cohen*, D. Simovici, T. Stepinski, the 19th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, Chicago, IL, November, 2011
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Genetically Enhanced Feature Selection of Discriminative Planetary Crater Image Features

J. P. Cohen*, S. Liu*, W. Ding, the 24th Australasian Joint Conference on Artificial Intelligence, Perth, Australia, December, 2011
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Machine Learning Approaches to Detecting Impact Craters in Planetary Images

T. F. Stepinski, W. Ding, R. Vilalta, Intelligent Data Analysis for Real-Life Applications: Theory and Practice, IGI Global, 2011
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Semi-Supervised Active Class Selection for Automatic Identification of Sub-Kilometer Craters

S. Liu*, W. Ding, T. Stepinski, 7th International Symposium on Image and Signal Processing and Analysis (ISPA 2011), Dubrovnik, Croatia, September, 2011
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Crater Detection Using Bayesian Classifiers and LASSO

Y. Wang*, W. Ding, K. Yu*, H. Wang, X. Wu, IEEE International Conference on Intelligent Computing and Integrated Systems, Guilin, Guangxi, China, October, 2011
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Biologically Inspired Model for Crater Detection

Y. Mu*, W. Ding, D. Tao, T.F. Stepinski, International Joint Conference on Neural Networks(IJCNN), San Jose, CA, August, 2011
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Sub-Kilometer Crater Discovery with Boosting and Transfer Learning

W. Ding, T. Stepinski, Y. Mu*, L. Bandeira*, R. Vilalta, Y. Wu*, Z. Lu*, T. Cao*, X. Wu, ACM Transactions on Intelligent Systems and Technology, 2011
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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
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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.
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Individuals Involved

Joseph Paul Cohen, Siyi Liu, Wei Ding, Yang Mu, T. F. Stepinski, Y. Wang, J. Wang

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