Andreas Piter, Mahmud Haghshenas Haghighi, Mahdi Motagh
{"title":"用于交通基础设施监测的哨兵-1 InSAR 的挑战与机遇","authors":"Andreas Piter, Mahmud Haghshenas Haghighi, Mahdi Motagh","doi":"10.1007/s41064-024-00314-x","DOIUrl":null,"url":null,"abstract":"<p>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.</p>","PeriodicalId":56035,"journal":{"name":"PFG-Journal of Photogrammetry Remote Sensing and Geoinformation Science","volume":"11 1","pages":""},"PeriodicalIF":2.1000,"publicationDate":"2024-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Challenges and Opportunities of Sentinel-1 InSAR for Transport Infrastructure Monitoring\",\"authors\":\"Andreas Piter, Mahmud Haghshenas Haghighi, Mahdi Motagh\",\"doi\":\"10.1007/s41064-024-00314-x\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>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. 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Challenges and Opportunities of Sentinel-1 InSAR for Transport Infrastructure Monitoring
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.
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).