城区地表形变差分TomoSAR监测方法

doi:10.11947/j.AGCS.2016.20160113

测绘学报 ›› 2016, Vol. 45 ›› Issue (12): 1413-1422.doi: 10.11947/j.AGCS.2016.20160113

城区地表形变差分TomoSAR监测方法

王爱春^1,2,3, 向茂生¹, 汪丙南¹

1. 中国科学院电子学研究所微波成像技术国家级重点实验室, 北京 100190;
2. 中国科学院大学, 北京 100049;
3. 中国资源卫星应用中心, 北京 100094

收稿日期:2016-03-25 修回日期:2016-10-19 出版日期:2016-12-20 发布日期:2017-01-02
通讯作者: 向茂生 E-mail:xms@mail.ie.ac.cn
作者简介:王爱春(1981-),男,博士生,工程师,研究方向为多基线干涉SAR处理方法及应用。E-mail:wangaichun@cresda.com
基金资助:
国家发改委卫星及应用产业发展专项（[2012]2083）

Method of Monitoring Urban Area Deformation Based on Differential TomoSAR

WANG Aichun^1,2,3, XIANG Maosheng¹, WANG Bingnan¹

1. National Key Laboratory of Microwave Imaging Technology, Institute of Electronics, Chinese Academy of Sciences, Beijing 100190, China;
2. University of Chinese Academy of Sciences, Beijing 100049, China;
3. China Center for Resources Satellite Data and Application, Beijing 100094, China

Received:2016-03-25 Revised:2016-10-19 Online:2016-12-20 Published:2017-01-02
Supported by:
National Development and Reform Commission Satellite and Application Development Projects (No.[2012]2083)

摘要/Abstract

摘要： 压缩感知技术（CS）的差分TomoSAR技术解决了中高分辨率SAR数据在城区出现的叠掩问题，实现了城区地表形变信息的重构，但是该方法仅利用了目标的稀疏特性并没有考虑目标的结构特性，对具有这两种特性的目标进行重构时其性能较差。针对这一问题，本文采用联合Khatri-Rao子空间和块压缩感知（KRS-BCS），提出了一种差分SAR层析成像方法。该方法依据目标的结构特性和重构观测矩阵具有的Khatri-Rao积性质，将稀疏结构目标的差分TomoSAR问题转化为Khatri-Rao子空间下的BCS问题，然后对目标进行块稀疏的l₁/l₂范数最优化求解，最后通过理论分析和仿真试验对分辨能力和重构估计性能进行了定性和定量评价，仿真结果表明本文所采用的KRS-BCS方法不仅保持了高分辨率的优点，而且有效地降低了虚假目标出现的概率，大幅度提高了散射点准确重构概率，切实可行地解决了CS方法的不足。应用实例研究中，利用34景Envisat卫星ASAR时间序列影像对日本千叶县茂原市城区进行地表形变监测，并以一等水准点和实时测量的GPS站点观测数据作为参考形变结果进行验证，试验结果表明采用KRS-BCS方法反演的结果与参考形变结果保持了良好的一致性且形变速率整体偏差也较小，实现了较高精度的城区地表形变估计。

关键词: 城区地表形变, 差分TomoSAR技术, Khatri-Rao子空间, 块压缩感知

Abstract: While the use of differential TomoSAR based on compressive sensing (CS) makes it possible to solve the layover problem and reconstruct the deformation information of an observed urban area scene acquired by moderate-high resolution SAR satellite, the performance of the reconstruction decreases for a sparse and structural observed scene due to ignoring the structural characteristics of the observed scene. To deal with this issue, the method for differential SAR tomography based on Khatri-Rao subspace and block compressive sensing (KRS-BCS) is proposed. The proposed method changes the reconstruction of the sparse and structural observed scene into a BCS problem under Khatri-Rao subspace, using the structure information of the observed scene and Khatri-Rao product property of the reconstructed observation matrix for differential TomoSAR, such that the KRS-BCS problem is efficiently solved with a block sparse l₁/l₂ norm optimization signal model, and the performance of resolution capability and reconstruction estimation is compared and analyzed qualitatively and quantitatively by the theoretical analysis and the simulation experiments, all of the results show the propose KRS-BCS method practicably overcomes the problems of CS method, as well as, quite maintains the high resolution characteristics, effectively reduces the probability of false scattering target and greatly improves the reconstruction accurate of scattering point. Finally, the application is taking the urban area of the Mobara(in Chiba, Japan) as the test area and using 34 ENVISAT-ASAR images, the accuracy is verifying with the reference deformations derived from first level point data and GPS tracking data, the results show the trend is consistent and the overall deviation is small between reconstruction deformations of the propose KRS-BCS method and the reference deformations, and the accuracy is high in the estimation of the urban area deformation.

Key words: urban area deformation, differential TomoSAR, Khatri-Rao subspace, block compressive sensing

中图分类号:

P237

王爱春, 向茂生, 汪丙南. 城区地表形变差分TomoSAR监测方法[J]. 测绘学报, 2016, 45(12): 1413-1422.

WANG Aichun, XIANG Maosheng, WANG Bingnan. Method of Monitoring Urban Area Deformation Based on Differential TomoSAR[J]. Acta Geodaetica et Cartographica Sinica, 2016, 45(12): 1413-1422.

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城区地表形变差分TomoSAR监测方法

Method of Monitoring Urban Area Deformation Based on Differential TomoSAR

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