基于BiLSTM-CNN的地基SAR永久散射体选取
Permanent scatterers selection of ground-based SAR based on BiLSTM-CNN
陈姣 1刘毓 2邓云开 3金重阳 1杨志航2
作者信息
- 1. 重庆三峡学院电子与信息工程学院 重庆 404130;北京理工大学重庆创新中心 重庆 401120
- 2. 重庆三峡学院电子与信息工程学院 重庆 404130
- 3. 北京理工大学信息与电子学院雷达技术研究所 北京 100081
- 折叠
摘要
针对地基合成孔径雷达(SAR)形变测量中,常规永久散射体(PS)选取方法在时间欠相干复杂场景下,PS选取数量、质量难以满足形变测量需求的问题.提出了一种基于双向长短期记忆-卷积神经网络(BiLSTM-CNN)的PS选取方法,该方法采用幅度离差与幅度联合选取正、负样本构建训练数据集,并把干涉相位、幅度差分与相关系数作为数据集的时序特征,然后利用BiLSTM和多尺度CNN分别学习PS全局时序特征及局部时序特征,再通过多头自注意力机制(MHSA)对全局和局部时序特征进行加权融合学习,最后进行特征概率映射以构建PS分类模型.利用重庆市万州区九道拐雷达监测数据对所提方法性能进行实验分析,结果表明该方法改善了网络准确度、F1 分数、召回率、精确度等指标,提高了雷达图像PS选取数量及质量.
Abstract
Aiming at the ground-based synthetic aperture radar(SAR)deformation measurement,the conventional permanent scatterer(PS)selection method is difficult to meet the deformation measurement requirements in terms of the quantity and quality of PS selection in the time-incoherent and complex scenarios.The article proposes a PS selection method based on bi-directional long short-term memory-convolutional neural network(BiLSTM-CNN),which uses amplitude dispersion and amplitude to jointly select positive and negative samples to construct the training dataset,and takes the interfering phase,amplitude divergence and correlation coefficient as the temporal features of the dataset,and then learns the PS global temporal features and the PS local temporal features by using the BiLSTM and the multi-scale CNN,respectively,and then the global and local temporal features are weighted and fused to learn by the multi-head self-attention(MHSA),and finally feature probability mapping is carried out in order to construct the PS classification model.The performance of the proposed selection method is experimentally analyzed by using the radar monitoring data of Jiudaoquan in Wanzhou District,Chongqing Municipality,and the results show that the method improves the network accuracy,F1 score,recall,precision,and other indexes,and improves the quantity and quality of radar image PS selection.
关键词
地基SAR/永久散射体/双向长短期记忆/卷积神经网络/多头自注意力机制Key words
ground-based synthetic aperture radar/permanent scatterer/bi-directional long short-term memory/convo-lutional neural network/multi-head self-attention引用本文复制引用
基金项目
国家重点研发计划子课题(2021YFB3901400)
重庆市教委科学技术项目(KJQN202101215)
出版年
2024
NETL
NSTL
万方数据