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《Journal of Software》 2010-04
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Semi-Supervised SAR Target Recognition Based on Laplacian Regularized Least Squares Classification

ZHANG Xiang-Rong1,2, YANG Chun1,2, JIAO Li-Cheng1,2 1(Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China, Xidian University, Xi’an 710071, China) 2(Institute of Intelligent Information Processing, Xidian University, Xi’an 710071, China)  
A Synthetic Aperture Radar (SAR) target recognition approach based on KPCA (kernel principal component analysis) and Laplacian regularized least squares classification is proposed. KPCA feature extraction method can not only extract the main characteristics of target, but also reduce the input dimension effectively. Laplacian regularized least squares classification is a semi-supervised learning method. In the target recognition process, training set is treated as labeled samples and test set as unlabeled samples. Since the test samples are considered in the learning process, high recognition accuracy is obtained. Experimental results on MSTAR (moving and stationary target acquisition and recognition) SAR datasets show its good performance and robustness to azimuth interval. Compared with template matching, support vector machine and regularized least squares learning method, the proposed method gets more SAR target recognition accuracy. In addition, the effect of the number of labeled points on target identification performance is analyzed at different conditions.
【Fund】: 国家自然科学基金Nos.60803097 60672126;; 国家高技术研究发展计划(863)Nos.2008AA01Z125 2007AA12Z223 2006AA01Z107;; “十一五”预研项目No.51307040103;; 国家教育部科学技术研究重点项目No.108115~~
【CateGory Index】: TN957.52
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