基于极化分解和膨胀卷积的极化SAR地物分类  

Terrain classification of Pol-SAR based on dilated convolution and polarization decomposition

作　　者：张泽宇 刘畅 ZHANG Ze-yu;LIU Chang(Aerospace Information Research Institute,Chinese Academy of Sciences,Beijing 100190,China;University of Chinese Academy of Sciences,Beijing 100049,China;Institute of Electronics,Chinese Academy of Sciences,Beijing 100190)

机构地区：[1]中国科学院空天信息创新研究院,北京100190 [2]中国科学院大学,北京100049 [3]中国科学院电子学研究所,北京100190

出　　处：《微电子学与计算机》2020年第12期70-76,共7页Microelectronics & Computer

基　　金：国家自然科学基金(61471340);国家重点研发计划(2017YFB0503001)。

摘　　要：针对基于深度学习的极化SAR地物分类中存在的标注数据少且未能利用SAR极化分解特征的问题,提出了一种基于极化分解和膨胀卷积的极化SAR地物分类方法.在低采样率下训练基于像素分类的卷积神经网络(CNN),将其卷积层参数迁移到同结构的膨胀卷积网络(DCNN),解决了膨胀卷积网络训练数据不足的问题;然后将反应地物散射特性的极化分解特征与包含高维空间语义信息的膨胀卷积特征图进行联合,使用联合特征构建随机森林(RF)进行分类.实验表明,特征图和极化分解的联合特征能够实现更为精确的分类,且由于引入膨胀卷积和随机森林,算法具有很高的实时性.In order to solve the problems of the lack of labeling data and the failure to make use of the polarization decomposition feature of SAR in Pol-SAR terrain classification methods based on deep learning,a polarimetric SAR terrain classification method based on dilated convolution and polarization decomposition is proposed.The convolutional neural network(CNN)based on pixel classification was trained at low sampling rate,and its convolutional layer parameters were transferred to the expansive convolutional network(DCNN)with the same structure,thus solving the problem of insufficient training data of the expansive convolutional network.Then the polarization decomposition feature reflecting the scattering characteristics of ground objects is combined with the expansive convolution feature map containing the high-dimensional spatial semantic information,and the random forest(RF)is constructed by using the joint feature for classification.Experiments show that the alliance of polarization decomposition features and feature maps can achieve more accurate classification,and the algorithm has high real-time performance due to the dilated convolution and random forest.

关 键 词：Pol-SAR 膨胀卷积 极化分解 地物分类 

分 类 号：TP753[自动化与计算机技术—检测技术与自动化装置]