Modelling of instrument repositioning errors in discontinuous Multi-Campaign Ground-Based SAR (MC-GBSAR) deformation monitoring
Modelling of instrument repositioning errors in discontinuous Multi-Campaign Ground-Based SAR (MC-GBSAR) deformation monitoring
复制标题
不连续多活动地基 SAR (MC-GBSAR) 变形监测中仪器重新定位误差的建模
DOI：
10.1016/j.isprsjprs.2019.08.019
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发表时间：
2019-11
影响因子：
12.7
通讯作者：
Jon Mills
中科院分区：
文献类型：
--
作者：
Zheng Wang;Zhenhong Li;Jon Mills
Ground-Based SAR (GBSAR) data acquisition in discontinuous mode can be useful for monitoring events whereby deformations become significant over relatively long periods. However, repositioning errors often occur in repeated campaigns and cause inaccuracies in discontinuous GBSAR deformation monitoring. This study firstly investigates the characteristics and quantifies the effects of repositioning errors. Three effects are identified: image shifts, geometric phase ramps, and topographic phase errors. The remainder of this paper then focuses on the modelling and removal of these effects. Images are automatically co-registered through amplitude-based feature matching with a sub-pixel co-registration precision. Whereas traditionally the geometric and topographic phase errors are simply considered as low-frequency signals and removed by filtering, this study presents accurate models for removing these errors. The geometric phase ramps are removed by recovering a 2nd-order polynomial function of the range and azimuth image coordinates. A linear model is introduced to correct the topographic effect without knowing the spatial baseline between different campaigns. Finally, a new combined approach is proposed by merging the geometric and topographic correction models together with a rigorous atmospheric correction model. A new interferometric processing chain is thereby developed on the basis of the proposed combined model for discontinuous Multi-Campaign GBSAR (MC-GBSAR) deformation monitoring. The feasibility of this chain is demonstrated through its application to both synthetic and real-world GBSAR data comprising both moderate and considerable repositioning errors.
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影响因子：
8
作者：
K. Lau;H. Weng
通讯作者：
K. Lau;H. Weng
影响因子：
7.4
作者：
B. Lowry;Francisco Gómez;Wei Zhou;M. Mooney;B. Held;J. Grasmick
通讯作者：
B. Lowry;Francisco Gómez;Wei Zhou;M. Mooney;B. Held;J. Grasmick
DOI：
10.1109/jstars.2014.2366711
发表时间：
2015-03-01
影响因子：
5.5
作者：
Iglesias, Ruben;Aguasca, Albert;Pipia, Luca
通讯作者：
Pipia, Luca
影响因子：
5
作者：
Luo, Qingli;Perissin, Daniele;Jia, Youliang
通讯作者：
Jia, Youliang
DOI：
10.1109/tgrs.2004.836792
发表时间：
2004-11-01
影响因子：
8.2
作者：
Luzi, G;Pieraccini, M;Atzeni, C
通讯作者：
Atzeni, C