Falabella Francesco, Serio Carmine, Zeni Giovanni, Pepe Antonio
School of Engineering, The University of Basilicata, 85100 Potenza, Italy.
National Research Council of Italy, Institute for the Electromagnetic Sensing of the Environment (CNR-IREA), 80124 Napoli, Italy.
Sensors (Basel). 2020 Feb 18;20(4):1103. doi: 10.3390/s20041103.
This paper concentrates on the study of the Weighted Least-squares (WLS) approaches for the generation of ground displacement time-series through Differential Interferometric SAR (DInSAR) methods. Usually, within the DInSAR framework, the Weighted Least-squares (WLS) techniques have principally been applied for improving the performance of the phase unwrapping operations as well as for better conveying the inversion of sequences of unwrapped interferograms to generate ground displacement maps. In both cases, the identification of low-coherent areas, where the standard deviation of the phase is high, is requested. In this paper, a WLS method that extends the usability of the Multi-Temporal InSAR (MT-InSAR) Small Baseline Subset (SBAS) algorithm in regions with medium-to-low coherence is presented. In particular, the proposed method relies on the adaptive selection and exploitation, pixel-by-pixel, of the medium-to-high coherent interferograms, only, so as to discard the noisy phase measurements. The selected interferometric phase values are then inverted by solving a WLS optimization problem. Noteworthy, the adopted, pixel-dependent selection of the "good" interferograms to be inverted may lead the available SAR data to be grouped into several disjointed subsets, which are then connected, exploiting the Weighted Singular Value Decomposition (WSVD) method. However, in some critical noisy regions, it may also happen that discarding of the incoherent interferograms may lead to rejecting some SAR acquisitions from the generated ground displacement time-series, at the cost of the reduced temporal sampling of the data measurements. Thus, variable-length ground displacement time-series are generated. The mathematical framework of the developed technique, which is named Weighted Adaptive Variable-lEngth (WAVE), is detailed in the manuscript. The presented experiments have been carried out by applying the WAVE technique to a SAR dataset acquired by the COSMO-SkyMed (CSK) sensors over the Basilicata region, Southern Italy. A cross-comparison analysis between the conventional and the WAVE method has also been provided.
本文着重研究通过差分干涉合成孔径雷达（DInSAR）方法生成地面位移时间序列的加权最小二乘（WLS）方法。通常，在DInSAR框架内，加权最小二乘（WLS）技术主要用于提高相位解缠操作的性能，以及更好地进行解缠干涉图序列的反演以生成地面位移图。在这两种情况下，都需要识别相位标准差较高的低相干区域。本文提出了一种WLS方法，该方法扩展了多视InSAR（MT-InSAR）小基线子集（SBAS）算法在中低相干区域的适用性。具体而言，所提出的方法仅逐像素地自适应选择和利用中高相干干涉图，以舍弃有噪声的相位测量值。然后，通过求解WLS优化问题来反演所选的干涉相位值。值得注意的是，采用与像素相关的方式选择要反演的“好”干涉图可能会导致可用的SAR数据被分组为几个不相交的子集，然后利用加权奇异值分解（WSVD）方法将它们连接起来。然而，在一些关键的噪声区域，也可能出现舍弃非相干干涉图会导致从生成的地面位移时间序列中排除一些SAR采集数据的情况，代价是数据测量的时间采样减少。因此，生成了可变长度的地面位移时间序列。手稿中详细介绍了所开发技术的数学框架，该技术名为加权自适应可变长度（WAVE）。通过将WAVE技术应用于意大利南部巴斯利卡塔地区由COSMO-SkyMed（CSK）传感器获取的SAR数据集进行了本文所展示的实验。还提供了传统方法与WAVE方法之间的交叉对比分析。
