摘要
针对传统的单幅主影像生成的相干图在干涉叠加过程中相干性低的问题,文章提出了一种基于多幅主影像的合成孔径雷达差分干涉测量(differential interferometric synthetic aperture radar, D-InSAR)加权叠加的方法.该方法利用较少的合成孔径雷达(synthetic aperture radar, SAR)图像生成干涉图集,采用加权叠加方法避免相干性低的相干图参与叠加,从而实现对某一区域高精度长时间的形变监测.首先选取多幅主影像生成多个干涉图集合;其次将得到的多组相干图按权重进行叠加;最终得到多个不同时段的地表形变平均速率图.文中选取美国圣迭戈县地区2019年7月至2019年12月9景Sentinel-1A数据进行实验验证,通过将实验结果与地下水监测数据对比验证实验精度.实验结果表明,这种方法不仅减少了实验所需影像数量,同时也提高了参与叠加的干涉图质量,在提高监测精度上获得比传统D-InSAR更好的监测结果.
To solve the problem of low coherence in the interference superposition of the coherence map generated by the traditional single main image, this study proposes a weighted superposition method based on the differential interferometric synthetic aperture radar(D-InSAR) with multiple main images. This method uses fewer SAR images to generate interferograms, and relies on the weighted superposition method to avoid the involvement of coherence maps with low coherence in the superposition, so as to achieve high-accuracy and long-time deformation monitoring in a certain area. Firstly, multiple main images are selected to generate multiple interferogram sets. Secondly, the multiple sets of coherence maps are superposed according to the weight. Finally, the average velocity maps of surface deformation in different time periods are obtained. In this study, Sentinel-1 A data of 9 scenarios from July 2019 to December 2019 were selected from San Diego City in the United States for experimental verification, and the experimental accuracy was verified by the comparison of the experimental results with groundwater monitoring data. The experimental results show that this method not only reduces the number of images required for the experiment, but also improves the quality of the interferograms involved in the superposition. It produces better monitoring results than the traditional D-InSAR in improving the monitoring accuracy.
作者
李克冲
董张玉
杨学志
LI Ke-Chong;DONG Zhang-Yu;YANG Xue-Zhi(School of Computer Science and Information Engineering,Hefei University of Technology,Hefei 230601,China;Key Laboratory of Knowledge Engineering with Big Data(Hefei University of Technology),Ministry of Education,Hefei 230601,China;School of Software,Hefei University of Technology,Hefei 230601,China)
出处
《计算机系统应用》
2022年第1期21-28,共8页
Computer Systems & Applications
基金
安徽省科技攻关计划(202004a07020030)。
