%0 Journal Article
%T Semi-Supervised SAR Target Recognition Based on Laplacian Regularized Least Squares Classification<br>基于Laplacian正则化最小二乘的半监督SAR目标识别
%A ZHANG Xiang-Rong
%A YANG Chun
%A JIAO Li-Cheng
%A <br>张向荣
%A 阳春
%A 焦李成
%J 软件学报
%D 2010
%I 
%X 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.
%K KPCA (kernel principal component analysis)
%K semi-supervised learning
%K Laplacian regularized least squares classification
%K SAR (synthetic aperture radar) target recognition<br>核主成分分析
%K 半监督学习
%K 拉普拉斯正则化最小二乘分类
%K SAR目标识别
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=7735F413D429542E610B3D6AC0D5EC59&aid=E6FC38FB3109C5FC028A27B9624ADAB0&yid=140ECF96957D60B2&vid=659D3B06EBF534A7&iid=E158A972A605785F&sid=CD26609C367AC9C8&eid=6341CCF6B158C5F9&journal_id=1000-9825&journal_name=软件学报&referenced_num=1&reference_num=29