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The Stripmap–ScanSAR SBAS Approach to Fill Gaps in Stripmap Deformation Time Series With ScanSAR Data

机译:Stripmap–ScanSAR SBAS方法用ScanSAR数据填补Stripmap变形时间序列中的间隙

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We present a simple approach to jointly exploit stripmap and ScanSAR acquisitions to generate differential synthetic aperture radar interferometry (DInSAR) time series. In particular, we extend the capability of the Small BAseline Subset (SBAS) approach to compute deformation time series from a set of stripmap images by filling possible temporal gaps in the available SAR data sequence with ScanSAR acquisitions. The starting point of our approach is the raw data focusing step, which is properly carried out to align the characteristics of the ScanSAR images to those of the stripmap ones. To achieve this task, we exploit stripmap processing codes to focus both SAR data types, the ScanSAR ones being processed on a burst-by-burst basis, accounting also for possible differences of the pulse repetition frequency with respect to that of the stripmap data. The coherent combination of the focused bursts generates phase-preserved ScanSAR images with the same output geometry and pixel spacing as the stripmap ones. This allows a straightforward implementation of the next steps of the SBAS processing chain, including the interferogram generation operation. In this case, we concentrate on a selection of small baseline (SB) stripmap-stripmap multilook interferograms identified through a Delaunay triangulation, which are complemented with a set of hybrid SB stripmap-ScanSAR interferograms. This interferogram selection permits us to develop an effective phase unwrapping algorithm based on a two-step processing strategy. Finally, the whole data set of unwrapped interferograms is inverted through the SBAS technique to retrieve the final deformation time series, including both stripmap and ScanSAR data. The proposed stripmap-ScanSAR SBAS process ing approach is particularly attractive because it is very easy to implement since it requires only limited modifications with respect to the conventional stripmap-based SBAS algorithm. Our approach has been applied to descending and ascending h-n-nybrid stripmap-ScanSAR data sets of Envisat/ASAR C-band acquisitions from the Big Island of Hawaii. In spite of not including any common-band azimuthal filtering, which would account for the ScanSAR burst spectral properties at the expense of the algorithm simplicity, the presented results show that we may retrieve DInSAR time series with an accuracy ranging between 5 and 10 mm, consistent with previous C-band data analyses using only stripmap data.
机译:我们提出了一种简单的方法来联合利用带状图和ScanSAR采集来生成差分合成孔径雷达干涉测量(DInSAR)时间序列。特别是,我们通过使用ScanSAR采集来填充可用SAR数据序列中可能存在的时间间隙,从而扩展了小型BAseline子集(SBAS)方法从一组带状图图像计算变形时间序列的功能。我们方法的出发点是原始数据聚焦步骤,可以正确执行该步骤,以使ScanSAR图像的特征与带状图的特征对齐。为了实现此任务,我们利用条带图处理代码来集中两种SAR数据类型,其中,ScanSAR是逐脉冲处理的,同时也考虑了脉冲重复频率相对于条带图数据的可能差异。聚焦突发的相干组合会生成相位保留的ScanSAR图像,其输出几何形状和像素间距与带状图相同。这允许直接执行SBAS处理链的后续步骤,包括干涉图生成操作。在这种情况下,我们集中于通过Delaunay三角测量确定的小基线(SB)带状图-条状图多视点干涉图的选择,并辅以一组混合SB带状图-ScanSAR干涉图。干涉图的选择使我们能够基于两步处理策略开发有效的相位展开算法。最后,通过SBAS技术反转整个未包裹干涉图的数据集,以检索最终变形时间序列,包括带状图和ScanSAR数据。所提出的带状图-ScanSAR SBAS处理方法特别吸引人,因为它非常容易实现,因为它相对于传统的基于带状图的SBAS算法仅需要有限的修改。我们的方法已应用于从夏威夷大岛获取的Envisat / ASAR C波段h-n-nybrid stripmap-ScanSAR数据集的下降和上升。尽管不包括任何会以算法简单性为代价来解释ScanSAR突发频谱特性的公共频带方位滤波,但提出的结果表明,我们可以以5至10 mm的精度检索DInSAR时间序列,与以前仅使用带状图数据的C波段数据分析保持一致。

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