文档介绍：∗
基于 Laplacian 正则化最小二乘的半监督 SAR 目标识别
张向荣 1,2+,
春 1,2, 焦李成 1,2
阳
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)
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)
+ Corresponding author: E-mail: xrzhang@
Zhang XR, Yang C, Jiao LC. Semi-Supervised SAR target recognition based on Laplacian regularized least
squares classification. Journal of Software, 2010,21(4):586−596. /1000-9825/
Abstract: A Synthetic Aperture Radar (SAR) target recognition approach based on KPCA (kernel ponent 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.
Key words: KPCA (kernel ponent analysis); semi-supervised learning; Laplacian regularized least
squares classification; SAR (synthetic aperture radar) ta