基于KPCA准则的SAR目标特征提取与识别
SAR Automatic target recognition based on KPCA criterion
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摘要: 该文给出了一种基于 KPCA(Kernel Principal Component Analysis)和 SVM(SupportVector Machine)的合成孔径雷达(Synthetic Aperture Radar,SAR)目标特征提取与识别方法。该方法在非线性空间内利用线性 PCA(Principal Component Analysis)准则提取目标特征并由 SVM分类器完成目标识别。基于美国国防高级研究计划署(Defense Advanced Research Project Agency,DARPA)和空军研究室(Air Force Research Laboratory,AFRL)提供的实测 SAR地面目标数据的实验结果表明,该文方法不但能够提高识别率,具有良好的推广能力,同时还降低了对方位估计精度的要求,是一种有效的 SAR目标特征提取与识别方法。Abstract: In this paper, SAR ATR (Synthetic Aperture Radar Automatic Target Recogni-tion) approach based on KPCA (Kernel Principal Component Analysis) is proposed. KPCA first maps the input data into some feature space using kernel functions and then performs lin-ear PCA on the mapped data. It takes the principal components in nonlinear space as sample features, then SVM classifier is used to classify targets. Experimental results with MSTAR SAR, data sets provided by the US DARPA/AFRL (Defense Advanced Research Projects Agency/Air Force Research Laboratory) show a better performance of classification and generalization.
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