Land Cover Classification with Generated Full-Polarization SAR Data From Single-Polarization SAR Data Using Deep Convolutional Neural Network

被引:0
|
作者
Duan, Yan-Cui [1 ]
Chen, Si-Wei [1 ]
机构
[1] Natl Univ Def Technology, State Key Lab Complex Elect Environm Effects Elec, Changsha, Peoples R China
来源
13TH EUROPEAN CONFERENCE ON SYNTHETIC APERTURE RADAR, EUSAR 2021 | 2021年
基金
中国国家自然科学基金;
关键词
D O I
暂无
中图分类号
TP7 [遥感技术];
学科分类号
081102 ; 0816 ; 081602 ; 083002 ; 1404 ;
摘要
With the ability to acquire full polarization information, polarimetric Synthetic Aperture Radar (PolSAR) is widely used in various applications. However, the single-polarization SAR data is more available in reality. In this paper, we present an approach of classifying land covers with generated PolSAR data from single-polarization SAR data using deep convolutional neural network (CNN). Experimental results on multi-temporal UAVSAR data show that the generated PolSAR data is visually and quantitatively close to real PolSAR data. Comparative experiments for land cover classification demonstrate that the generated PolSAR data contains more information and can improve the classification accuracy greatly.
引用
收藏
页码:1086 / 1089
页数:4
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