摘要
针对目前有关极化合成孔径雷达(Polarimetric Synthetic Aperture Radar,PolSAR)的飞机目标检测算法虚警较多、自适应性较差的问题,给出一种复杂大场景中PolSAR图像多特征分类的飞机目标检测方法。该方法分为线下分类器训练和飞机目标检测两部分。使用Filter特征选择结合穷举法筛选出分类性能高的飞机极化特征训练SVM(Support Vector Machine,SVM)分类器;利用异化散射功率提取疑似飞机目标,进一步提取多个极化特征送入SVM分类获得检测结果。利用UAVSAR系统采集的多幅实测数据进行实验,并与现有的PolSAR图像飞机目标检测算法进行对比,结果表明该方法能够有效检测出飞机目标,并且虚警和漏警较少,方法自适应性有所提高。
Aiming at the problem of high false alarm and poor flexibility in aircraft detection by PolSAR image,this paper presents a new aircraft detection algorithm for PolSAR image with large complex scenes.The performance of the detection process consists of SVM classifier training and detection of aircraft target.In the procedure of SVM training,multiple optimized polarization features of the aircrafts are selected through the combination of Filter feature selection and exhaustive method to train the SVM classifier.In the detection process,suspected aircraft targets are selected from the large PolSAR image by threshold method with dissimilation scattering power,then multiple polarization features extracted from the suspected aircraft targets will be input to the SVM classifier to obtain the detection result.The multiple testing data collected by UAVSAR system and the comparison with the existing algorithm of PolSAR image indicate that the method presented in this paper could effectively detect aircraft targets with less false and missed alarm as well as improved flexibility.
作者
卢晓光
周波
韩萍
韩宾宾
Lu Xiaoguang;Zhou Bo;Han Ping;Han Binbin(Tianjin Key Lab for Advanced Signal Processing,CAUC,Tianjin 300300,China)
出处
《信号处理》
CSCD
北大核心
2019年第4期563-573,共11页
Journal of Signal Processing
基金
国家自然科学基金项目(61571442)
国家重点研发计划(2016YFB0502405)
关键词
极化合成孔径雷达
特征提取
飞机目标检测
SVM分类器
polarimetric synthetic aperture radar
feature extraction
aircraft detection
support vector machine
