Challenges and Opportunities of Sentinel-1 InSAR for Transport Infrastructure Monitoring

IF 2.1 4区 地球科学 Q3 IMAGING SCIENCE & PHOTOGRAPHIC TECHNOLOGY
Andreas Piter, Mahmud Haghshenas Haghighi, Mahdi Motagh
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Abstract

Monitoring displacement at transport infrastructure using Sentinel‑1 Interferometric Synthetic Aperture Radar (InSAR) faces challenges due to the sensor’s medium spatial resolution, which limits the pixel coverage over the infrastructure. Therefore, carefully selecting coherent pixels is crucial to achieve a high density of reliable measurement points and to minimize noisy observations. This study evaluates the effectiveness of various pixel selection methods for displacement monitoring within transport infrastructures. We employ a two-step InSAR time series processing approach. First, high-quality first-order pixels are selected using temporal phase coherence (TPC) to estimate and correct atmospheric contributions. Then, a combination of different pixel selection methods is applied to identify coherent second-order pixels for displacement analysis. These methods include amplitude dispersion index (ADI), TPC, phase linking coherence (PLC), and top eigenvalue percentage (TEP), targeting both point-like scatterer (PS) and distributed scatterer (DS) pixels. Experiments are conducted in two case studies: one in Germany, characterized by dense vegetation, and one in Spain, with sparse vegetation. In Germany, the density of measurement points was approximately 30 points/km², with the longest segment of the infrastructure without any coherent pixels being 2.8 km. In Spain, the density of measurement points exceeded 500 points/km², with the longest section without coherent pixels being 700 meters. The results indicate that despite the challenges posed by medium-resolution data, the sensor is capable of providing adequate measurement points when suitable pixel selection methods are employed. However, careful consideration is necessary to exclude noisy pixels from the analysis. The findings highlight the importance of choosing a proper method tailored to infrastructure characteristics. Specifically, combining TPC and PLC methods offers a complementary set of pixels suitable for displacement measurements, whereas ADI and TEP are less effective in this context. This study demonstrates the potential of Sentinel‑1 InSAR for capturing both regional-scale and localized displacements at transport infrastructure.

Abstract Image

用于交通基础设施监测的哨兵-1 InSAR 的挑战与机遇
使用哨兵-1 干涉合成孔径雷达(InSAR)监测交通基础设施的位移面临挑战,因为传感器的空间分辨率较低,限制了对基础设施的像素覆盖。因此,要获得高密度的可靠测量点并最大限度地减少噪声观测,精心选择相干像素至关重要。本研究评估了各种像素选择方法在交通基础设施位移监测中的有效性。我们采用了两步 InSAR 时间序列处理方法。首先,利用时相相干性(TPC)选择高质量的一阶像素,以估计和修正大气贡献。然后,结合不同的像素选择方法,确定相干的二阶像素,进行位移分析。这些方法包括振幅色散指数 (ADI)、时相相干性 (TPC)、相位链接相干性 (PLC) 和顶特征值百分比 (TEP),针对点状散射体 (PS) 和分布式散射体 (DS) 像素。实验在两个案例研究中进行:一个在植被茂密的德国,另一个在植被稀疏的西班牙。在德国,测量点密度约为 30 点/平方公里,基础设施中没有任何连贯像素的最长路段为 2.8 公里。在西班牙,测量点密度超过 500 点/平方公里,没有连贯像素的最长路段为 700 米。结果表明,尽管中等分辨率数据带来了挑战,但如果采用适当的像素选择方法,传感器还是能够提供足够的测量点。不过,在分析时需要仔细考虑如何排除噪声像素。研究结果强调了根据基础设施特点选择适当方法的重要性。具体来说,将 TPC 和 PLC 方法结合起来可提供一组适合位移测量的互补像素,而 ADI 和 TEP 在这方面的效果较差。这项研究证明了 Sentinel-1 InSAR 在捕捉交通基础设施的区域和局部位移方面的潜力。
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来源期刊
CiteScore
8.20
自引率
2.40%
发文量
38
期刊介绍: PFG is an international scholarly journal covering the progress and application of photogrammetric methods, remote sensing technology and the interconnected field of geoinformation science. It places special editorial emphasis on the communication of new methodologies in data acquisition and new approaches to optimized processing and interpretation of all types of data which were acquired by photogrammetric methods, remote sensing, image processing and the computer-aided interpretation of such data in general. The journal hence addresses both researchers and students of these disciplines at academic institutions and universities as well as the downstream users in both the private sector and public administration. Founded in 1926 under the former name Bildmessung und Luftbildwesen, PFG is worldwide the oldest journal on photogrammetry. It is the official journal of the German Society for Photogrammetry, Remote Sensing and Geoinformation (DGPF).
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