王岩飞1,,
1.中国科学院空天信息创新研究院 北京 100190
2.中国科学院大学电子电气与通信工程学院 北京 101408
基金项目:国家重点研发计划(2017YFB0503001)
详细信息
作者简介:王睿川(1994–)，男，四川绵阳人，中国科学院空天信息创新研究院博士研究生，研究方向为SAR和极化SAR图像分割和解译、目标检测和识别等
王岩飞(1963–)，男，辽宁沈阳人，中国科学院空天信息创新研究院研究员，博士生导师，主要研究方向为微波成像雷达理论方法及应用、数字信号处理等
通讯作者:王岩飞 yfwang@mail.ie.ac.cn
责任主编：邹焕新 Corresponding Editor: ZOU Huanxin中图分类号:TN958
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出版历程
收稿日期:2021-06-11
修回日期:2021-06-22
网络出版日期:2021-07-05
Terrain Classification of Polarimetric SAR Images Using Semi-supervised Spatial-channel Selective Kernel Network
WANG Ruichuan1, 2,WANG Yanfei1,,
1. Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China
2. School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 101408, China
Funds:The National Key Research and Development Program (2017YFB0503001)
More Information
Corresponding author:WANG Yanfei, yfwang@mail.ie.ac.cn
摘要
摘要:针对极化合成孔径雷达(极化SAR)图像地物分类中标注样本数量少的问题，该文提出一种基于空间-通道选择性卷积核全卷积网络(SCSKFCN)和预选-联合优化半监督学习(SPUO)的极化SAR图像地物分类方法。SCSKFCN通过使用空间和通道注意力机制，对不同感受野的特征进行自适应加权融合，有效提升了模型的分类性能。SPUO能够高效地利用标注样本，挖掘无标注样本中蕴含的信息。它采用K-Wishart距离进行样本预选并生成伪标签，然后在联合优化阶段使用真实标注样本和伪标注样本同时对模型进行优化。在模型优化过程中，SPUO对伪标注样本进行两步验证并筛选可靠的伪标注样本参与优化。实验结果表明，该方法能够在只使用少量标注样本的条件下实现高精度、高效率的极化SAR图像地物分类。
关键词:极化SAR图像地物分类/
全卷积网络/
注意力机制/
半监督学习/
空间-通道选择性卷积核网络
Abstract:In this paper, a Spatial-Channel Selective Kernel Fully Convolutional Network (SCSKFCN) and a Semi-supervised Preselection-United Optimization (SPUO) method are proposed for polarimetric Synthetic Aperture Radar (SAR) image classification. Integrated with spatial-channel attention mechanism, SCSKFCN adaptively fuses features that have different sizes of reception field, and achieves promising classification performance. SPUO can efficiently extract information contained in unlabeled samples according to annotated samples. It utilizes K-Wishart distance to preselect unlabeled samples for pseudo label generation, and then optimizes SCSKFCN with both labeled and pseudo labeled samples. During the training process of SCSKFCN, a two-step verification mechanism is applied on pseudo labeled samples to reserve reliable samples for united optimization. The experimental results show that the proposed SCSKFCN-SPUO can achieve promising performance and efficiency using limited number of annotated pixels.
Key words:Terrain classification of PolSAR images/
Fully convolutional network/
Attention mechanism/
Semi-supervised learning/
Spatial-Channel Selective Kernel Network (SCSKCN)
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