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SAR Target Feature Extraction and Recognition Based on Improved Clustering-based Discriminant Analysis |
Hu Li-ping; Liu Hong-wei; Wu Shun-jun |
National Laboratory of Radar Signal Processing, Xidian University, Xi'an 710071, China |
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Abstract In many literatures, Synthetic Aperture Radar (SAR) data is usually supposed to obey the unimodal distribution, unsuitable in the applications. To overcome the limitation, an Improved Clustering-based Discriminant Analysis (ICDA) method is proposed, which assumes the distribution of each class for SAR data is multimodal, a more reasonable and practical assumption. The detailed procedure of ICDA is to first partition each class of the SAR data into multiple clusters via the fast global k-means clustering algorithm, and then try to find the projection vectors such that the projections of every pair of clusters from different classes are well separated while the within-cluster scatter is minimized. Experimental results performing on SAR ground stationary targets based the Moving and Stationary Target Acquisition and Recognition (MSTAR) public database show that ICDA has better classification capabilities of three true objects classes and rejection capabilities of two confusers classes.
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Received: 09 September 2008
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