Use of shadows for detection of earthquake-induced collapsed buildings in high-resolution satellite imagery

Tong, Xiaohua; Lin, Xiaofei; Feng, Tiantian; Xie, Huan; Liu, Shijie; Hong, Zhonghua; Chen, Peng

doi:10.1016/j.isprsjprs.2013.01.012

Cited by 65 publications

(26 citation statements)

References 30 publications

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“…Generally, we can categorize those methods into two types as: detecting the collapsed buildings (1) by measuring the difference between the preseismic and postseismic digital surface models (hereafter called as DSMs) created from the aerial images of target area, and (2) by checking changes (including height or shadow) in the ground 3D models created by the light detection and ranging (LIDAR) system. Tong et al [6,7] brought an accurate detection algorithm of the collapsed building by comparing the changes in preseismic and postseismic digital elevation model (hereafter called as DEMs) which are created by the high-resolution satellite IKONOS stereo images. In [6], they detected the collapsed buildings by comparing the height changes in the DEM before and after earthquake, where huge height changes correspond to a collapsed building.…”

Section: Related Workmentioning

confidence: 99%

“…In [6], they detected the collapsed buildings by comparing the height changes in the DEM before and after earthquake, where huge height changes correspond to a collapsed building. While in [7], if the shadow of a building changes greatly before and after the earthquake, it indicates that such a building is a collapsed one.…”

Section: Related Workmentioning

confidence: 99%

“…Therefore, without the human prior knowledge, when applied to a full aerial image, the performance of this method is suspected. Since covering all the researches about detecting the collapsed buildings is beyond the scope of this paper, more detailed survey could be found in [6,7].…”

Section: Related Workmentioning

confidence: 99%

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Detection of collapsed buildings with the aerial images captured from UAV

Hua

Shang

et al. 2015

Sci. China Inf. Sci.

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In this paper, we present a method of detecting the collapsed buildings with the aerial images which are captured by an unmanned aerial vehicle (UAV) for the postseismic evaluation. Different from the conventional methods that apply the satellite images or the high-altitude UAV for the coarse disaster evaluation over large area, the purpose of this work is to achieve the accurate detection of collapsed buildings in small area from low altitude. By combining the motion and appearance features of collapsed buildings extracted from successive aerial images, each pixel in the input image will be measured by a statistical method where the background pixels will be penalized and the ones of collapsed buildings will be assigned with high value. The candidates of collapsed buildings will be established by integrating the extracted feature points into local groups with the online clustering algorithm. To reduce the false alarm caused by the complex background noise, each predicted candidate will be further verified by the temporal tracking framework where both the trajectory and the appearance of a candidate will be measured. The candidate of collapsed buildings that can survive through long time will be considered as true positive, otherwise rejected as a false alarm. Through extensive experiments, the efficiency and the effectiveness of proposed algorithm have been proved.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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Detection of collapsed buildings with the aerial images captured from UAV

Hua

Shang

et al. 2015

Sci. China Inf. Sci.

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“…It helps in well detection of different objects visually and also automated detection of collapsed buildings after natural disasters e.g. earthquakes and 3D reconstruction of buildings (Huang and Kwoh, 2007;Tong et al, 2013). While it ruins the contrast of objects casted by shadow and consequently classification, objects detection and automated stereo image matching (Shahtahmassebi et al, 2013;Tsai, 2006).…”

Section: Introductionmentioning

confidence: 99%

A New Object-Based Framework to Detect Shodows in High-Resolution Satellite Imagery Over Urban Areas

Tatar

Saadatseresht

Arefi

et al. 2015

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

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Commission VII, WG VII/4 KEY WORDS: Shadow Detection, Spectral Index, High resolution satellite imagery, Segmentation, Object-based, Majority Voting. ABSTRACT:In this paper a new object-based framework to detect shadow areas in high resolution satellite images is proposed. To produce shadow map in pixel level state of the art supervised machine learning algorithms are employed. Automatic ground truth generation based on Otsu thresholding on shadow and non-shadow indices is used to train the classifiers. It is followed by segmenting the image scene and create image objects. To detect shadow objects, a majority voting on pixel-based shadow detection result is designed. GeoEye-1 multi-spectral image over an urban area in Qom city of Iran is used in the experiments. Results shows the superiority of our proposed method over traditional pixel-based, visually and quantitatively.

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“…If the condition of building damage is known immediately after an earthquake, the accuracy of casualty estimation within a short time frame can be improved greatly. High-resolution satellite imagery (HRSI) has recently been used to detect the difference in the height of one building following an earthquake (Teeuw et al, 2013;Lu et al, 2013;Tong et al, 2013;Huang et al, 2013), owing to its large coverage, low cost, short revisit time and adaptable capability of stereo imaging (Tao et al, 2004;Tack et al, 2012). This paper aimed to build a casualty estimation model based on remote sensing.…”

Section: Introductionmentioning

confidence: 99%

Application and prospect of a high-resolution remote sensing and geo-information system in estimating earthquake casualties

Feng

Hong

et al. 2014

Nat. Hazards Earth Syst. Sci.

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Abstract. An accurate estimation of a casualty rate is critical in response to earthquake disasters, and could allow an increase in the survival rate. Building damage is considered to be a major cause of earthquake casualties in developing countries. High-resolution satellite imagery (HRSI) can be used to detect the building damage in a period of a short time. This makes it possible to use a model to estimate earthquake casualties immediately after the occurrence of an earthquake. With respect to the capability of HRSI, this study built a new model for estimating the casualty rate in an earthquake disaster based on remote sensing and a geographical information system. Three groups of earthquake data, the 2003 Bam earthquake, the 2008 Wenchuan earthquake, and the 2010 Yushu earthquake, were used to evaluate this model. The results indicated that our new model significantly improved the accuracy in predicting the casualty rate. The parameters used in the model vary between developed and developing countries. This study could provide valuable information for a more efficient rescue operation in response to earthquakes.

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Use of shadows for detection of earthquake-induced collapsed buildings in high-resolution satellite imagery

Cited by 65 publications

References 30 publications

Detection of collapsed buildings with the aerial images captured from UAV

Detection of collapsed buildings with the aerial images captured from UAV

A New Object-Based Framework to Detect Shodows in High-Resolution Satellite Imagery Over Urban Areas

Application and prospect of a high-resolution remote sensing and geo-information system in estimating earthquake casualties

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