摘要
给出了一种特征选择与深度学习相结合的极化合成孔径雷达(polarimetricsyntheticapertureradar,PolSAR)图像有监督分类算法。该算法首先根据极化SAR图像数据以及目标分解获取原始特征参数集,然后利用随机森林(RandomForest,RF)方法对特征参数集进行重要性评估,并根据特征重要性排名选择最优极化特征。以最优极化特征为输入,通过卷积神经网络(convolutionalneuralnetwork,CNN)学习多层特征信息,再利用训练好的网络模型对极化SAR图像进行分类。利用美国AIRSAR机载系统采集的实测数据进行实验,并同已有经典有监督分类算法进行比较,结果表明本文算法能够选取有效的极化特征,最终得到较为准确的分类效果。
A supervised classification algorithm with feature selection and deep learning for polarimetric synthetic aperture radar(PolSAR) image is proposed in this paper. Firstly, an original feature parameter set is extracted from the polarization SAR image data and the target decomposition. Then the random forest method is used to evaluate the importance of the feature parameter set. After that, the optimal polarization features are obtained according to the feature importance rank.Taking the optimal polarization feature as the input, the multi-layer feature information is learned by the convolutional neural network(CNN), and the PolSAR image is classified by the trained network model. Experiments are carried out using the measured data collected by the U.S. AIRSAR airborne system, and the results are compared with which of the existing classical supervised classification algorithm. The results show that the proposed algorithm can select effective polarization features and finally obtain more accurate classification results.
作者
韩萍
孙丹丹
Han Ping;Sun Dandan(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6期972-978,共7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
特征选择
深度学习
随机森林
卷积神经网络
有监督分类
polarimetric synthetic aperture radar
feature selection
deep learning
random forest
convolutional neural network
supervised classification
