Crater detection using Bayesian classifiers and LASSO
Date
2013
Authors
Wang, Y.
Ding, W.
Yu, K.
Wang, H.
Wu, X.
Editors
Kopcho, J.
Kurzawa, C.
McPherson, G.
Kurzawa, C.
McPherson, G.
Advisors
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Conference paper
Citation
2013 IEEE Conference Anthology, 2013 / Kopcho, J., Kurzawa, C., McPherson, G. (ed./s), pp.1-5
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Conference Name
2011 IEEE International Conference on Intelligent Computing and Integrated Systems (lCISS) (18 Nov 2011 - 20 Nov 2011 : Guilin, China)
Abstract
Surveying a large amount of small sub-kilometer craters in planetary images is a challenging task due to their non-distinguishable features. In this paper, we integrate the LASSO (Least Absolute Shrinkage and Selection Operator) method with the Bayesian network classifier and propose an L1 Regularized Bayesian Network Classifier (L1-BNC) algorithm for this task. The L1-BNC algorithm uses the LASSO method not only to deal with high-dimensional crater features, but also to give a crater feature order for constructing a Bayesian network classifier. Our framework is evaluated on a large Martian image of 37,500 × 56,250m2. Experimental results demonstrate that this proposed method gets higher prediction accuracy than the existing crater detection algorithms.
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Copyright 2013 IEEE