Coherence- and Amplitude-Based Analysis of Seismogenic Damage in Bam, Iran, Using ENVISAT ASAR Data

Arciniegas, Gustavo; Bijker, W.; Kerle, N.; Tolpekin, V.A.

doi:10.1109/tgrs.2006.883149

Cited by 84 publications

(67 citation statements)

References 22 publications

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“…Coherence-and σ 0 -based analyses were compared by Arciniegas et al (2007) with ENVISAT/A-SAR data. The overall accuracy for detecting damaged urban areas from the change in coherence was 44.5 %, better than the one based upon absolute change of σ 0 (41.2 %).…”

Section: Open Accessmentioning

confidence: 99%

Detection of damaged urban areas using interferometric SAR coherence change with PALSAR-2

et al. 2016

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The interferometric SAR coherence-change technique with coherence filter and polarization (HH and HV) has been used to detect the parts of buildings damaged by the 2015 Gorkha Earthquake. A survey of the building damage was conducted in every house to evaluate the detection accuracy in the Khokana and Sankhu urban areas in the Kathmandu Valley of Nepal. The damaged parts of the urban area were adequately detected using coherence-change (∆γ) values obtained before the earthquake (γ pre ) and during the inter-seismic stage of the earthquake (γ int ). The use of a coherence filter effectively increased overall accuracy by ~2.1 to 7.0 % with HH polarization. The incorporation of HV polarization marginally increased the accuracy (~0.9 to 1.2 %). It was confirmed that road damage due to liquefaction was also observed using the interferometric SAR coherence-change detection technique. The classification accuracy was lower (27.1-35.1 %) for areas that were damaged. However, higher accuracy (97.8-99.2 %) was achieved for areas that were damage-free, in ∆γ obtained from HH and HV polarization with a coherence filter. This helped to identify the damaged urban areas (using this technique) immediately after occurrence of an earthquake event.

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Section: Open Accessmentioning

confidence: 99%

Detection of damaged urban areas using interferometric SAR coherence change with PALSAR-2

et al. 2016

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“…However, structural damage mapping is an entirely different challenge. Numerous studies have explored the utility of image data (optical, radar, and airborne laser scanning [LiDAR] data, and both vertical and oblique imagery) (e.g., Kerle et al, 2008;Dell'Acqua and Gamba, 2012;Arciniegas et al, 2007). Many strategies to automate damage detection have been tested, but complex pattern recognition problems remain at the edge of what is possible via computational analysis.…”

Section: How Can Mapping Instructions Be Conveyed In the Most Comprehmentioning

confidence: 99%

Collaborative damage mapping for emergency response: the role of Cognitive Systems Engineering

Kerle

Hoffman

2013

Nat. Hazards Earth Syst. Sci.

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Abstract. Remote sensing is increasingly used to assess disaster damage, traditionally by professional image analysts. A recent alternative is crowdsourcing by volunteers experienced in remote sensing, using internet-based mapping portals. We identify a range of problems in current approaches, including how volunteers can best be instructed for the task, ensuring that instructions are accurately understood and translate into valid results, or how the mapping scheme must be adapted for different map user needs. The volunteers, the mapping organizers, and the map users all perform complex cognitive tasks, yet little is known about the actual information needs of the users. We also identify problematic assumptions about the capabilities of the volunteers, principally related to the ability to perform the mapping, and to understand mapping instructions unambiguously. We propose that any robust scheme for collaborative damage mapping must rely on Cognitive Systems Engineering and its principal method, Cognitive Task Analysis (CTA), to understand the information and decision requirements of the map and image users, and how the volunteers can be optimally instructed and their mapping contributions merged into suitable map products. We recommend an iterative approach involving map users, remote sensing specialists, cognitive systems engineers and instructional designers, as well as experimental psychologists.

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“…For example, the 2003 Bam Iran earthquake showed the superiority of the coherence-based method in sensitivity, though both the ground truth data and the satellite data did not have a high enough spatial resolution [5][6][7]. One possible reason for insufficient ground truth data is that estimating the damage level of every building was of a low priority compared with rescuing and recovering operations.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2018

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Abstract:In this paper, evaluation results are presented for multi-temporal interferometric coherence analysis using a Synthetic Aperture Radar (SAR) for damage assessment in an urban area. The latest space-borne SARs potentially have a high enough spatial resolution to assess individual buildings. However, interferometric coherence analysis has not been evaluated for its limitation in sensitivity and size of damaged buildings. In particular, the correlation between the coherence analysis and the damage level referred to by architectural assessments has been an open question. In this paper, analytical results using ALOS-2 PALSAR-2 datasets are presented from the 2016 Kumamoto earthquakes in Japan. For reference, building damage was assessed throughout the central urban area and specifically at a catastrophically damaged district. The results show that the buildings should be larger than a window size of the coherence for damage detection, and the damage level should be larger than Level-2 of 5, classified with the European Macroseismic Scale 1998 (EMS-98).

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Coherence- and Amplitude-Based Analysis of Seismogenic Damage in Bam, Iran, Using ENVISAT ASAR Data

Cited by 84 publications

References 22 publications

Detection of damaged urban areas using interferometric SAR coherence change with PALSAR-2

Detection of damaged urban areas using interferometric SAR coherence change with PALSAR-2

Collaborative damage mapping for emergency response: the role of Cognitive Systems Engineering

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

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