Sensitivity and Limitation in Damage Detection for Individual Buildings Using InSAR Coherence—A Case Study in 2016 Kumamoto Earthquakes

Natsuaki, Ryo; Nagai, Hiroto; Tomii, Naoya; Tadono, Takeo

doi:10.3390/rs10020245

Cited by 24 publications

(17 citation statements)

References 37 publications

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“…The threshold is generally defined manually to reduce the effect of temporal and spatial decorrelations. In [39], the authors found that the buildings which are larger than 200m 2 can be evaluated by setting the threshold 0.3. When the buildings exceed the threshold, they were moderately or severely damaged when they are classified by EMS-98 scheme [40].…”

Section: Interferometric Coherence Analysismentioning

confidence: 99%

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L-Band SAR Disaster Monitoring for Harbor Facilities Using Interferometric Analysis

Natsuaki¹

2019

Advanced Remote Sensing Technology for Synthetic Aperture Radar Applications, Tsunami Disasters, and Infrastructure

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Synthetic aperture radar (SAR) has become a major tool for disaster monitoring. Its all-weather capability enables us to monitor the affected area soon after the event happens. Since the first launch of spaceborne SAR, its amplitude images have been widely used for disaster observations. Nowadays, an accurate orbit control and scheduled frequent observations enable us to perform interferometric analysis of SAR (InSAR) and the use of interferometric coherence. Especially for L-band SAR, its long-lasting temporal coherence is an advantage to perform precise interferometric coherence analysis. In addition, recent high resolution SAR images are found to be useful for observing relatively small targets, e.g., individual buildings and facilities. In this chapter, we present basic theory of SAR observation, interferometric coherence analysis for the disaster monitoring, and its examples for the harbor facilities. In the actual case, DInSAR measurement could measure the subsidence of the quay wall with 3 cm error.

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Section: Interferometric Coherence Analysismentioning

confidence: 99%

“…Region U and V, moderately damaged and collapsed buildings, can be detected by interferometric coherence analysis too. Its sensitivity is discussed precisely in [39].…”

Section: Scattering Mechanisms Of Harbor Facilitiesmentioning

confidence: 99%

L-Band SAR Disaster Monitoring for Harbor Facilities Using Interferometric Analysis

Natsuaki¹

2019

Advanced Remote Sensing Technology for Synthetic Aperture Radar Applications, Tsunami Disasters, and Infrastructure

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“…We conducted Interferometry Synthetic Aperture Radar (InSAR) observations together with GPS/GIS data analysis and geophysical modelling to assess the vulnerability of Ulsan to surface deformation, and focused on the relationship between recent (2015-2018) and future potential seismic activities and the plausible risks of industrial infrastructure. InSAR techniques have been employed to investigate the effects of seismic activity and surface deformation in a large number of studies; it is a well-demonstrated method for determining potential hazards, particularly earthquakes [22][23][24], and for evaluating the associated damage [25][26][27]. However, our aim was to investigate the effects of relatively minor earthquakes and to monitor the potential damage from accumulated subtle strain over highly sensitive infrastructure facilities.…”

Section: Of 23mentioning

confidence: 99%

Seismic Surface Deformation Risks in Industrial Hubs: A Case Study from Ulsan, Korea, Using DInSAR Time Series Analysis

et al. 2019

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The unprecedentedly strong 2016 Gyeongju and 2017 Pohang earthquakes on the Korean Peninsula aroused public concern regarding seismic hazards previously considered improbable. In this study, we investigated the effects of recent seismic activity close to the epicenters of both earthquakes in the heavy industrial complex of Ulsan. This was performed using Sentinel-1 InSAR time series data combined with on-site GPS observations and background GIS data. The interpretations revealed ongoing topographic deformation of a fault line and surrounding geological units of up to 15 mm/year. Postseismic migrations through the fault line, coupled with the two earthquakes, were not significant enough to pose an immediate threat to the industrial facilities or the residential area. However, according to InSAR time series analyses and geophysical modelling, strain from the independent migration trend of a fault line and eventual/temporal topographic changes caused by potential seismic friction could threaten precisely aligned industrial facilities, especially chemical pipelines. Therefore, we conducted probabilistic seismic hazard and stress change analyses over surrounding areas of industrial facilities employing modelled fault parameters based on InSAR observations. These demonstrate the potential of precise geodetic survey techniques for constant monitoring and risk assessment of heavy industrial complexes against seismic hazards by ongoing fault activities.

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“…Furthermore, these two parameters are sometimes combined to conduct building damage detection [20,21]. Due to the development of high-spatialresolution (HR) and very-high-spatial-resolution (VHR) SAR images, an increasing number of change detection methods have been proposed to detect building damage at the individual building level [22][23][24][25][26]. In addition, deep learning-based methods for building change detection have been proposed using VHR SAR images [25,27].…”

Section: Introductionmentioning

confidence: 99%

Building Damage Detection Based on OPCE Matching Algorithm Using a Single Post-Event PolSAR Data

et al. 2021

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Synthetic aperture radar (SAR) is an effective tool in detecting building damage. At present, more and more studies detect building damage using a single post-event fully polarimetric SAR (PolSAR) image, because it permits faster and more convenient damage detection work. However, the existence of non-buildings and obliquely-oriented buildings in disaster areas presents a challenge for obtaining accurate detection results using only post-event PolSAR data. To solve these problems, a new method is proposed in this work to detect completely collapsed buildings using a single post-event full polarization SAR image. The proposed method makes two improvements to building damage detection. First, it provides a more effective solution for non-building area removal in post-event PolSAR images. By selecting and combining three competitive polarization features, the proposed solution can remove most non-building areas effectively, including mountain vegetation and farmland areas, which are easily confused with collapsed buildings. Second, it significantly improves the classification performance of collapsed and standing buildings. A new polarization feature was created specifically for the classification of obliquely-oriented and collapsed buildings via development of the optimization of polarimetric contrast enhancement (OPCE) matching algorithm. Using this developed feature combined with texture features, the proposed method effectively distinguished collapsed and obliquely-oriented buildings, while simultaneously also identifying the affected collapsed buildings in error-prone areas. Experiments were implemented on three PolSAR datasets obtained in fully polarimetric mode: Radarsat-2 PolSAR data from the 2010 Yushu earthquake in China (resolution: 12 m, scale of the study area: ); ALOS PALSAR PolSAR data from the 2011 Tohoku tsunami in Japan (resolution: 23.14 m, scale of the study area: ); and ALOS-2 PolSAR data from the 2016 Kumamoto earthquake in Japan (resolution: 5.1 m, scale of the study area: ). Through the experiments, the proposed method was proven to obtain more than 90% accuracy for built-up area extraction in post-event PolSAR data. The achieved detection accuracies of building damage were 82.3%, 97.4%, and 78.5% in Yushu, Ishinomaki, and Mashiki town study sites, respectively.

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Sensitivity and Limitation in Damage Detection for Individual Buildings Using InSAR Coherence—A Case Study in 2016 Kumamoto Earthquakes

Cited by 24 publications

References 37 publications

L-Band SAR Disaster Monitoring for Harbor Facilities Using Interferometric Analysis

L-Band SAR Disaster Monitoring for Harbor Facilities Using Interferometric Analysis

Seismic Surface Deformation Risks in Industrial Hubs: A Case Study from Ulsan, Korea, Using DInSAR Time Series Analysis

Building Damage Detection Based on OPCE Matching Algorithm Using a Single Post-Event PolSAR Data

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