Building-damage detection using post-seismic high-resolution SAR satellite data

Balz, Timo; Liao, Mingsheng

doi:10.1080/01431161003727671

Cited by 115 publications

(63 citation statements)

References 27 publications

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“…Balz and Liao [112] used the decrease of the corner reflections of damaged buildings to distinguish between damaged and undamaged buildings. However, this approach was only useful to detect strong damages.…”

Section: Post-event Methods For Sar Data Based Damage Assessmentmentioning

confidence: 99%

Rapid Damage Assessment by Means of Multi-Temporal SAR — A Comprehensive Review and Outlook to Sentinel-1

2014

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Fast crisis response after natural disasters, such as earthquakes and tropical storms, is necessary to support, for instance, rescue, humanitarian, and reconstruction operations in the crisis area. Therefore, rapid damage mapping after a disaster is crucial, i.e., to detect the affected area, including grade and type of damage. Thereby, satellite remote sensing plays a key role due to its fast response, wide field of view, and low cost. With the increasing availability of remote sensing data, numerous methods have been developed for damage assessment. This article gives a comprehensive review of these techniques focusing on multi-temporal SAR procedures for rapid damage assessment: interferometric coherence and intensity correlation. The review is divided into six parts: First, methods based on coherence; second, the ones using intensity correlation; and third, techniques using both methodologies combined to increase the accuracy of the damage assessment are reviewed. Next, studies using additional data (e.g., GIS and optical imagery) to support the damage assessment and increase its accuracy are reported. Moreover, selected studies on post-event SAR damage assessment techniques and examples of other applications of the interferometric coherence are presented. Then, the preconditions for a successful worldwide application of multi-temporal SAR methods for damage assessment and the limitations of current SAR satellite missions are reported. Finally, an outlook to the Sentinel-1 SAR mission shows possible solutions of these limitations, enabling a worldwide applicability of the presented damage assessment methods.

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Section: Post-event Methods For Sar Data Based Damage Assessmentmentioning

confidence: 99%

Rapid Damage Assessment by Means of Multi-Temporal SAR — A Comprehensive Review and Outlook to Sentinel-1

2014

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“…In normal urban situations, there are typically four zones in SAR amplitude images of buildings (Balz and Liao, 2010;Brunner et al, 2010): -corner reflection (double-bounce scattering, the strength of which strongly depends on the height of the building and its aspect angle, i.e. the orientation of the building relative to the viewing direction of the sensor), -layover (mixed zone of building-wall reflections and ground reflections), -roof (behind the layover, but often dark because of its material, though chimneys are visible), -shadow (building shadow is often inseparable from the surrounding area such as roads; shadows may also be overlapped by the layover of surrounding buildings).…”

Section: Buildings In Sarmentioning

confidence: 99%

An approach for detecting changes related to natural disasters using Synthetic Aperture Radar data

Milisavljević

Closson

Holecz

et al. 2015

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Commission VI, WG VI/4KEY WORDS: SAR, Natural disasters, Christchurch, Classification, Urban zones, Data fusion, Coherent change detection ABSTRACT:Land-cover changes occur naturally in a progressive and gradual way, but they may happen rapidly and abruptly sometimes. Very high resolution remote sensed data acquired at different time intervals can help in analyzing the rate of changes and the causal factors. In this paper, we present an approach for detecting changes related to disasters such as an earthquake and for mapping of the impact zones. The approach is based on the pieces of information coming from SAR (Synthetic Aperture Radar) and on their combination. The case study is the 22 February 2011 Christchurch earthquake. The identification of damaged or destroyed buildings using SAR data is a challenging task. The approach proposed here consists in finding amplitude changes as well as coherence changes before and after the earthquake and then combining these changes in order to obtain richer and more robust information on the origin of various types of changes possibly induced by an earthquake. This approach does not need any specific knowledge source about the terrain, but if such sources are present, they can be easily integrated in the method as more specific descriptions of the possible classes. A special task in our approach is to develop a scheme that translates the obtained combinations of changes into ground information. Several algorithms are developed and validated using optical remote sensing images of the city two days after the earthquake, as well as our own ground-truth data. The obtained validation results show that the proposed approach is promising.

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“…From the perspective of data used, researchers employed different sources of RS data, including Light Detection and Ranging (LiDAR) [3], Synthetic Aperture Radar (SAR) [4][5][6][7][8][9], and optical imagery [10][11][12]. Optical satellite imagery is one of the useful sources in building damage detection process.…”

Section: Literature Reviewmentioning

confidence: 99%

A Fuzzy-GA Based Decision Making System for Detecting Damaged Buildings from High-Spatial Resolution Optical Images

Janalipour

Mohammadzadeh

2017

Remote Sensing

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Abstract:In this research, a semi-automated building damage detection system is addressed under the umbrella of high-spatial resolution remotely sensed images. The aim of this study was to develop a semi-automated fuzzy decision making system using Genetic Algorithm (GA). Our proposed system contains four main stages. In the first stage, post-event optical images were pre-processed. In the second stage, textural features were extracted from the pre-processed post-event optical images using Haralick texture extraction method. Afterwards, in the third stage, a semi-automated Fuzzy-GA (Fuzzy Genetic Algorithm) decision making system was used to identify damaged buildings from the extracted texture features. In the fourth stage, a comprehensive sensitivity analysis was performed to achieve parameters of GA leading to more accurate results. Finally, the accuracy of results was assessed using check and test samples. The proposed system was tested over the 2010 Haiti earthquake (Area 1 and Area 2) and the 2003 Bam earthquake (Area 3). The proposed system resulted in overall accuracies of 76.88 ± 1.22%, 65.43 ± 0.29%, and 90.96 ± 0.15% over Area 1, Area 2, and Area 3, respectively. On the one hand, based on the concept of the proposed Fuzzy-GA decision making system, the automation level of this system is higher than other existing systems. On the other hand, based on the accuracy of our proposed system and four advanced machine learning techniques, i.e., bagging, boosting, random forests, and support vector machine, in the detection of damaged buildings, it seems that our proposed system is robust and efficient.

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Building-damage detection using post-seismic high-resolution SAR satellite data

Cited by 115 publications

References 27 publications

Rapid Damage Assessment by Means of Multi-Temporal SAR — A Comprehensive Review and Outlook to Sentinel-1

Rapid Damage Assessment by Means of Multi-Temporal SAR — A Comprehensive Review and Outlook to Sentinel-1

An approach for detecting changes related to natural disasters using Synthetic Aperture Radar data

A Fuzzy-GA Based Decision Making System for Detecting Damaged Buildings from High-Spatial Resolution Optical Images

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