Novel algorithms for pair and pixel selection and atmospheric error correction in multitemporal InSAR
Novel algorithms for pair and pixel selection and atmospheric error correction in multitemporal InSAR
复制标题
多时相 InSAR 中对和像素选择以及大气误差校正的新算法
DOI：
10.1016/j.rse.2022.113447
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发表时间：
2023
影响因子：
13.5
通讯作者：
Shirzaei, Manoochehr
中科院分区：
文献类型：
--
作者：
Lee, Jui-Chi;Shirzaei, Manoochehr
进入SAR的黄金时代始于2014年和2016年发射的Sentinel-1A/B卫星，重访时间为6-12天，在给定区域内可获得更大的高分辨率SAR图像堆栈，以执行时间序列分析。处理大堆叠尺寸的算法面临着几个挑战，包括由于信号去相关引起的干涉相位质量劣化、应用多视引起的相位闭合误差和对流层相位延迟。在这里，我们提出了一种改进的SBAS型算法，适合于处理一个大堆栈的SAR图像在任意分辨率。我们开发了一种新的对选择策略，该策略应用二进下采样结合广泛使用的Delaunay三角测量来确定最佳的干涉对集合，以最大限度地减少由于短寿命信号和闭合误差引起的系统误差。我们开发和应用一种新的统计框架，选择精英像素占分布和永久散射。此外，我们实现了一个新的对流层误差校正，利用光滑的2D样条识别和删除错误组件与分形结构。我们证明了算法的有效性，将其应用到3个大型数据集的哨兵-1 SAR图像测量非线性表面变形在各种地形。与独立的GNSS观测相比，我们发现，在农村/自然地形邻近圣安德烈亚斯断层在南加州，我们的方法产生的标准偏差为0.48厘米的上升和下降轨道的时间序列差异。而在城市地区，例如洛杉矶，与GNSS时间序列的标准偏差差为0.30 cm。
Entering the SAR's golden era began with the launch of Sentinel-1A/B satellites in 2014 and 2016 with 6–12 day revisit time, much larger stacks of high-resolution SAR images are available over a given area to perform time series analysis. Algorithms that deal with large stack sizes face several challenges, including interferometric phase quality degradation due to signal decorrelations, phase closure error caused by applied multilooking, and tropospheric phase delay. Here, we present an improved SBAS-type algorithm suitable for processing a large stack of SAR images at an arbitrary resolution. We develop a new pair selection strategy that applies dyadic downsampling combined with widely used Delaunay Triangulation to identify an optimal set of interferometric pairs that minimize systematic errors due to short-lived signals and closure errors. We develop and apply a novel statistical framework that selects elite pixels accounting for distributed and permanent scatterers. Also, we implement a new tropospheric error correction that takes advantage of smooth 2D splines to identify and remove error components with fractal-like structures. We demonstrate the effectiveness of the algorithms by applying them to 3 large datasets of Sentinel-1 SAR images measuring non-linear surface deformation over various terrains. Compared with independent GNSS observations, we find that over the rural/natural terrains adjacent to San Andreas fault in southern California, our approach yields a standard deviation of 0.48 cm for time series differences in both ascending and descending tracks. While in urban areas, such as Los Angeles, standard deviation difference with GNSS time series is 0.30 cm.
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影响因子：
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作者：
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影响因子：
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DOI：
--
发表时间：
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期刊：
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影响因子：
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作者：
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DOI：
10.1029/2018jb016765
发表时间：
2019-09
期刊：
Journal of Geophysical Research: Solid Earth
影响因子：
--
作者：
E. Tymofyeyeva;Y. Fialko;Junle Jiang;Xiaohua Xu;D. Sandwell;R. Bilham;T. Rockwell;Chelsea M. Blanton;Faith Burkett;A. Gontz;S. Moafipoor
通讯作者：
E. Tymofyeyeva;Y. Fialko;Junle Jiang;Xiaohua Xu;D. Sandwell;R. Bilham;T. Rockwell;Chelsea M. Blanton;Faith Burkett;A. Gontz;S. Moafipoor