基于Laplacian正则化最小二乘的半监督SAR目标识别  被引量:13

Semi-Supervised SAR Target Recognition Based on Laplacian Regularized Least Squares Classification

在线阅读下载全文

作  者:张向荣[1,2] 阳春[1,2] 焦李成[1,2] 

机构地区:[1]西安电子科技大学智能感知与图像理解教育部重点实验室,陕西西安710071 [2]西安电子科技大学智能信息处理研究所,陕西西安710071

出  处:《软件学报》2010年第4期586-596,共11页Journal of Software

基  金: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~~

摘  要:提出了一种基于核主成分分析(kernel principal component analysis,简称KPCA)和拉普拉斯正则化最小二乘(Laplacian regularized least squares,简称LapRLS)的合成孔径雷达(synthetic aperture radar,简称SAR)目标识别方法.KPCA特征提取方法不仅能够提取目标主要特征,而且有效地降低了特征维数.Laplacian正则化最小二乘分类是一种半监督学习方法,将训练集样本作为有标识样本,测试集样本作为无标识样本,在学习过程中将测试集样本包含进来以获得更高的识别率.在MSTAR实测SAR地面目标数据上进行实验,结果表明,该方法具有较高的识别率,并对目标角度间隔具有鲁棒性.与模板匹配法、支撑矢量机以及正则化最小二乘监督学习方法相比,具有更高的SAR目标识别正确率.此外,还通过实验分析了不同情况下有标识样本数目对目标识别性能的影响.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.

关 键 词:核主成分分析 半监督学习 拉普拉斯正则化最小二乘分类 SAR 目标识别 

分 类 号:TP391[自动化与计算机技术—计算机应用技术]

 

参考文献:

正在载入数据...

 

二级参考文献:

正在载入数据...

 

耦合文献:

正在载入数据...

 

引证文献:

正在载入数据...

 

二级引证文献:

正在载入数据...

 

同被引文献:

正在载入数据...

 

相关期刊文献:

正在载入数据...

相关的主题
相关的作者对象
相关的机构对象