Building damage detection based on single high-resolution remote sensing imagery

In this paper, a novel approach of building damaged detection is proposed using high resolution remote sensing images and 3D GIS-Model data. Traditional building damage detection method considers to detect damaged building due to earthquake, but little attention has been paid to analyze various building damaged types(e.g., trivial damaged, severely damaged and totally collapsed.) Therefore, we want to detect the different building damaged type using 2D and 3D feature of scenes because the real world we live in is a 3D space. The proposed method generalizes that the image geometric correction method firstly corrects the post-disasters remote sensing image using the 3D GIS model or RPC parameters, then detects the different building damaged types using the change of the height and area between the pre- and post-disasters and the texture feature of post-disasters. The results, evaluated on a selected study site of the Beichuan earthquake ruins, Sichuan, show that this method is feasible and effective in building damage detection. It has also shown that the proposed method is easily applicable and well suited for rapid damage assessment after natural disasters.

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AUTOMATIC BUILDING DAMAGE DETECTION METHOD USING HIGH-RESOLUTION REMOTE SENSING IMAGES AND 3D GIS MODEL

ISPRS Annals of Photogrammetry Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-annals-iii-8-43-2016 ◽

2016 ◽

Vol III-8 ◽

pp. 43-50 ◽

Cited By ~ 1

Author(s):

Jihui Tu ◽

Haigang Sui ◽

Wenqing Feng ◽

Zhina Song

Keyword(s):

Remote Sensing ◽

High Resolution ◽

Damage Detection ◽

Detection Method ◽

Texture Feature ◽

Correction Method ◽

Building Damage ◽

3D Gis ◽

Remote Sensing Images ◽

Gis Model

In this paper, a novel approach of building damaged detection is proposed using high resolution remote sensing images and 3D GIS-Model data. Traditional building damage detection method considers to detect damaged building due to earthquake, but little attention has been paid to analyze various building damaged types(e.g., trivial damaged, severely damaged and totally collapsed.) Therefore, we want to detect the different building damaged type using 2D and 3D feature of scenes because the real world we live in is a 3D space. The proposed method generalizes that the image geometric correction method firstly corrects the post-disasters remote sensing image using the 3D GIS model or RPC parameters, then detects the different building damaged types using the change of the height and area between the pre- and post-disasters and the texture feature of post-disasters. The results, evaluated on a selected study site of the Beichuan earthquake ruins, Sichuan, show that this method is feasible and effective in building damage detection. It has also shown that the proposed method is easily applicable and well suited for rapid damage assessment after natural disasters.

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Estimations for Percentage of Impervious Area by the Use of Satellite Remote Sensing Imagery

Water Science & Technology ◽

10.2166/wst.1994.0659 ◽

1994 ◽

Vol 29 (1-2) ◽

pp. 135-144 ◽

Cited By ~ 21

Author(s):

C. Deguchi ◽

S. Sugio

Keyword(s):

Remote Sensing ◽

High Resolution ◽

Satellite Imagery ◽

Satellite Remote Sensing ◽

Aerial Photographs ◽

Estimation Errors ◽

Remote Sensing Imagery ◽

Impervious Area ◽

Urbanized Areas

This study aims to evaluate the applicability of satellite imagery in estimating the percentage of impervious area in urbanized areas. Two methods of estimation are proposed and applied to a small urbanized watershed in Japan. The area is considered under two different cases of subdivision; i.e., 14 zones and 17 zones. The satellite imageries of LANDSAT-MSS (Multi-Spectral Scanner) in 1984, MOS-MESSR(Multi-spectral Electronic Self-Scanning Radiometer) in 1988 and SPOT-HRV(High Resolution Visible) in 1988 are classified. The percentage of imperviousness in 17 zones is estimated by using these classification results. These values are compared with the ones obtained from the aerial photographs. The percent imperviousness derived from the imagery agrees well with those derived from aerial photographs. The estimation errors evaluated are less than 10%, the same as those obtained from aerial photographs.

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Greenhouse Extraction from High-Resolution Remote Sensing Imagery with Improved Random Forest

IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium ◽

10.1109/igarss39084.2020.9324147 ◽

2020 ◽

Author(s):

Tianjing Feng ◽

Hairong Ma ◽

Xinwen Cheng

Keyword(s):

Remote Sensing ◽

High Resolution ◽

Random Forest ◽

Remote Sensing Imagery

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Multi-Scene Building Height Estimation Method Based on Shadow in High Resolution Imagery

Remote Sensing ◽

10.3390/rs13152862 ◽

2021 ◽

Vol 13 (15) ◽

pp. 2862

Author(s):

Yakun Xie ◽

Dejun Feng ◽

Sifan Xiong ◽

Jun Zhu ◽

Yangge Liu

Keyword(s):

Remote Sensing ◽

High Resolution ◽

Estimation Method ◽

Absolute Error ◽

Large Error ◽

Estimation Methods ◽

Height Estimation ◽

Building Height ◽

Remote Sensing Imagery ◽

High Resolution Imagery

Accurately building height estimation from remote sensing imagery is an important and challenging task. However, the existing shadow-based building height estimation methods have large errors due to the complex environment in remote sensing imagery. In this paper, we propose a multi-scene building height estimation method based on shadow in high resolution imagery. First, the shadow of building is classified and described by analyzing the features of building shadow in remote sensing imagery. Second, a variety of shadow-based building height estimation models is established in different scenes. In addition, a method of shadow regularization extraction is proposed, which can solve the problem of mutual adhesion shadows in dense building areas effectively. Finally, we propose a method for shadow length calculation combines with the fish net and the pauta criterion, which means that the large error caused by the complex shape of building shadow can be avoided. Multi-scene areas are selected for experimental analysis to prove the validity of our method. The experiment results show that the accuracy rate is as high as 96% within 2 m of absolute error of our method. In addition, we compared our proposed approach with the existing methods, and the results show that the absolute error of our method are reduced by 1.24 m-3.76 m, which can achieve high-precision estimation of building height.

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