Fast building damage mapping using a single post-earthquake PolSAR image: a case study of the 2010 Yushu earthquake

Zhai, Wei; Huang, Chunlin

doi:10.1186/s40623-016-0469-2

Cited by 27 publications

(16 citation statements)

References 35 publications

(33 reference statements)

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“…One approach is based on identifying physical polarimetric SAR features [6], [7]. The advantage of this method is that it analyzes the damage from the essence of remote sensing by exploring the physical model of microwave scattering.…”

mentioning

confidence: 99%

A Framework of Rapid Regional Tsunami Damage Recognition From Post-event TerraSAR-X Imagery Using Deep Neural Networks

Bai

Gao

Singh

et al. 2018

IEEE Geosci. Remote Sensing Lett.

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mentioning

confidence: 99%

A Framework of Rapid Regional Tsunami Damage Recognition From Post-event TerraSAR-X Imagery Using Deep Neural Networks

Bai

Gao

Singh

et al. 2018

IEEE Geosci. Remote Sensing Lett.

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“…Shen et al [32] recognized the DB based on feature template matching as the image retrieval method. Zhai et al [6] and Zhai and Huang [33] proposed certain polarimetric features, including the normalized difference of the dihedral component (NDDC) and the difference in the relative contribution change rate of scattering components before and after polarization orientation angle compensation (CR Dbl-Vol ), to extract the building damage information. Zhang et al [34] proposed a novel approach for building collapse information extraction based on the optimization of polarimetric contrast enhancement (OPCE) algorithm.…”

Section: Introductionmentioning

confidence: 99%

Building Damage Assessment Based on the Fusion of Multiple Texture Features Using a Single Post-Earthquake PolSAR Image

2019

Self Cite

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After a destructive earthquake, most of the casualties are brought about by building collapse. Our work is focused on using a single postevent PolSAR (full-polarimetric synthetic aperture radar) imagery to extract the building damage information for effective emergency decision-making. PolSAR data is subject to sunlight and contains richer backscatter information. The undamaged buildings whose orientation is not parallel to the SAR flight pass and the collapsed buildings share similar dominated scattering mechanisms, i.e., volume scattering, so they are easily confused. However, the two kinds of buildings have different textures. For a more accurate classification of damaged buildings and undamaged buildings, the OPCE (optimization of polarimetric contrast enhancement) algorithm is employed to enhance the contrast ratio of the textures for the two kinds of buildings and the precision-weighted multifeature fusion (PWMF) method is proposed to merge the multiple texture features. The experiment results show that the accuracy of the proposed novel method is improved by 8.34% compared to the traditional method. In general, the proposed PWMF method can effectively merge the multiple features and the overestimation of the building collapse rate can be reduced using the proposed method in this study.

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“…Compared with combined pre-with post-event data [15], single post-event PoISAR data is quicker and more convenient to assess building damage [16]. The potential of post-event SAR images has been demonstrated for building damage assessment after earthquakes [17][18][19]. LiDAR can provide a three-dimensional visualization of the damaged area, which is useful to automatically generate a damage map [20].…”

Section: Introductionmentioning

confidence: 99%

Identifying Collapsed Buildings Using Post-Earthquake Satellite Imagery and Convolutional Neural Networks: A Case Study of the 2010 Haiti Earthquake

Liu

Buchroithner

2018

Remote Sensing

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Earthquake is one of the most devastating natural disasters that threaten human life. It is vital to retrieve the building damage status for planning rescue and reconstruction after an earthquake. In cases when the number of completely collapsed buildings is far less than intact or less-affected buildings (e.g., the 2010 Haiti earthquake), it is difficult for the classifier to learn the minority class samples, due to the imbalance learning problem. In this study, the convolutional neural network (CNN) was utilized to identify collapsed buildings from post-event satellite imagery with the proposed workflow. Producer accuracy (PA), user accuracy (UA), overall accuracy (OA), and Kappa were used as evaluation metrics. To overcome the imbalance problem, random over-sampling, random under-sampling, and cost-sensitive methods were tested on selected test A and test B regions. The results demonstrated that the building collapsed information can be retrieved by using post-event imagery. SqueezeNet performed well in classifying collapsed and non-collapsed buildings, and achieved an average OA of 78.6% for the two test regions. After balancing steps, the average Kappa value was improved from 41.6% to 44.8% with the cost-sensitive approach. Moreover, the cost-sensitive method showed a better performance on discriminating collapsed buildings, with a PA value of 51.2% for test A and 61.1% for test B. Therefore, a suitable balancing method should be considered when facing imbalance dataset to retrieve the distribution of collapsed buildings.

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Fast building damage mapping using a single post-earthquake PolSAR image: a case study of the 2010 Yushu earthquake

Cited by 27 publications

References 35 publications

A Framework of Rapid Regional Tsunami Damage Recognition From Post-event TerraSAR-X Imagery Using Deep Neural Networks

A Framework of Rapid Regional Tsunami Damage Recognition From Post-event TerraSAR-X Imagery Using Deep Neural Networks

Building Damage Assessment Based on the Fusion of Multiple Texture Features Using a Single Post-Earthquake PolSAR Image

Identifying Collapsed Buildings Using Post-Earthquake Satellite Imagery and Convolutional Neural Networks: A Case Study of the 2010 Haiti Earthquake

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