广西科技大学学报2024，Vol.35Issue(2)：65-77,13.DOI:10.16375/j.cnki.cn45-1395/t.2024.02.009
一种基于深度学习的多基线InSAR高程反演方法
A multi-baseline InSAR elevation reconstruction method based on deep learning
摘要
Abstract
A multi-baseline interferometric synthetic aperture radar(InSAR)elevation reconstruction method based on deep learning and cluster analysis is proposed.This method uses deep learning neural network to classify the intercept information of the interferogram as the basis for judging the category attributes of the pixels,and accurately obtains the clustering center of the pixels,and then cluster analysis technique is used to obtain the elevation information of the observed scenes.The main steps are as follows:firstly,the intercept information of interferograms is obtained,and then the deep learning neural network is used to classify the intercept information of the interferograms.Secondly,the intercepts of the pixels classified into the same category predicted by the network are averaged as the clustering center of this category,which effectively avoids the misclassification caused by the traditional technique due to poor noise robustness.Finally,the cluster analysis technique is used to obtain the elevation information of the observed scenes.The simulation and measured experiment results show that the root-mean-square error of the proposed method is smaller and the reconstruction accuracy is higher than that of the traditional CA algorithm under different SNR.关键词
多基线InSAR/深度学习/聚类分析Key words
multi-baseline InSAR/deep learning/cluster analysis分类
天文与地球科学引用本文复制引用
周宇翀,谢先明..一种基于深度学习的多基线InSAR高程反演方法[J].广西科技大学学报,2024,35(2):65-77,13.基金项目
国家自然科学基金项目(62161003,41661092) （62161003,41661092）
广西自然科学基金项目(2018GXNSFAA281196)资助 （2018GXNSFAA281196）
