Synthetic aperture radar automatic target recognition using adaptive boosting

Yijun Sun; Zhipeng Liu; Sinisa Todorovic; Jian Li

doi:10.1117/12.602666

19 May 2005 Synthetic aperture radar automatic target recognition using adaptive boosting

Yijun Sun, Zhipeng Liu, Sinisa Todorovic, Jian Li

Proceedings Volume 5808, Algorithms for Synthetic Aperture Radar Imagery XII; (2005) https://doi.org/10.1117/12.602666
Event: Defense and Security, 2005, Orlando, Florida, United States

Abstract

We propose a novel automatic target recognition (ATR) system for classification of three types of ground vehicles in the MSTAR public release database. First, each image chip is pre-processed by extracting fine and raw feature sets, where raw features compensate for the target pose estimation error that corrupts fine image features. Then, the chips are classified by using the adaptive boosting (AdaBoost) algorithm with the radial basis function (RBF) net as the base learner. Since the RBF net is a binary classifier, we decompose our multiclass problem into a set of binary ones through the error-correcting output codes (ECOC) method, specifying a dictionary of code words for the set of three possible classes. AdaBoost combines the classification results of the RBF net for each binary problem into a code word, which is then "decoded" as one of the code words (i.e., ground-vehicle classes) in the specified dictionary. Along with classification, within the AdaBoost framework, we also conduct efficient fusion of the fine and raw image-feature vectors. The results of large-scale experiments demonstrate that our ATR scheme outperforms the state-of-the-art systems reported in the literature.

Citation Download Citation

Yijun Sun, Zhipeng Liu, Sinisa Todorovic, and Jian Li "Synthetic aperture radar automatic target recognition using adaptive boosting", Proc. SPIE 5808, Algorithms for Synthetic Aperture Radar Imagery XII, (19 May 2005); https://doi.org/10.1117/12.602666

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