利用高分辨率光学遥感图像检测震害损毁建筑物
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摘要
地震发生后,利用高分辨率遥感图像进行建筑物损毁检测,有利于快速评估灾害损失。在分析损毁建筑物梯度分布的基础上,提出了一种利用梯度局部空间统计检测震害损毁建筑物的方法。首先用Prewitt算子提取图像梯度信息;然后对梯度图像进行局部空间统计,统计各建筑物屋顶内部梯度的空间相关性,得到初步损毁检测结果;最后,在先验知识的指导下进行极小值分析和阴影检测,进一步修正建筑物损毁检测结果。分别以玉树地震后的Quickbird卫星遥感图像和盈江地震后的光学航空图像为例进行实验,结果表明,利用梯度局部空间统计检测震害损毁建筑物的方法效果优于传统损毁检测方法,总体精度达到80%以上,能够有效检测损毁建筑物。
Damaged buildings detection from high-resolution remote sensing image helps to quick disaster losses evaluation after the earthquake. This paper presents a new method to detect damaged buildings using spectral gradient local spatial statistics, based on the analysis of gradient distribution characteristics of damaged buildings in the high-resolution remote sensing image. Firstly, spectral gradient image is obtained by Prewitt gradient operator. Secondly, local spatial statistics is used to evaluate the spectral gradient correlation within the roofs, and to generate the preliminary results. At last, the post processing steps, including minimal value analysis and shadow detection, are taken to optimize preliminary results and obtain the final results. The experiment results using a Quickbird image of Yushu earthquake and optical aerial image of Yingjiang earthquake demonstrate the effectiveness of the proposed method, which provides an overall accuracy of higher than 80%, are better than traditional methods.
Damaged buildings detection from high-resolution remote sensing image helps to quick disaster losses evaluation after the earthquake. This paper presents a new method to detect damaged buildings using spectral gradient local spatial statistics, based on the analysis of gradient distribution characteristics of damaged buildings in the high-resolution remote sensing image. Firstly, spectral gradient image is obtained by Prewitt gradient operator. Secondly, local spatial statistics is used to evaluate the spectral gradient correlation within the roofs, and to generate the preliminary results. At last, the post processing steps, including minimal value analysis and shadow detection, are taken to optimize preliminary results and obtain the final results. The experiment results using a Quickbird image of Yushu earthquake and optical aerial image of Yingjiang earthquake demonstrate the effectiveness of the proposed method, which provides an overall accuracy of higher than 80%, are better than traditional methods.
引文
[1] Dong L, Shan J. A Comprehensive Review of Earthquake-Induced Building Damage Detection with Remote Sensing Techniques[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2013, 84(7): 85-99
[2] Sui Haigang, Hua Feng, Fan Yida, et al. Road Damage Extraction from High-Resolution SAR Image Road Based on GIS Data and Bayes Network[J]. Geomatics and Information Science of Wuhan University, 2016, 41(5): 578-583 (眭海刚, 华凤, 范一大, 等. 利用GIS与贝叶斯网络进行高分辨率SAR影像道路损毁信息提取[J]. 武汉大学学报\5信息科学版, 2016, 41(5): 578-583)
[3] Tong X, Lin X, Feng T, et al. Use of Shadows for Detection of Earthquake-Induced Collapsed Buil- dings in High-Resolution Satellite Imagery[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2013, 79(5): 53-67
[4] Turker M, San B T. Detection of Collapsed Buil- dings Caused by the 1999 Izmit, Turkey Earthquake Through Digital Analysis of Post-Event Aerial Photographs[J]. International Journal of Remote Sensing, 2004, 25(21): 4 701-4 714
[5] Iwasaki Y, Yamazaki F. Detection of Building Collapse from the Shadow Lengths in Optical Satellite Images[C].The 32nd Asian Conference on Remote Sensing, Taipei, China, 2011
[6] Turker M, Sumer E. Building-based Damage Detection due to Earthquake Using the Watershed Segmentation of the Post-Event Aerial Images[J]. International Journal of Remote Sensing, 2008, 29(11): 3 073-3 089
[7] Guler M A, Turker M. Detection of the Earthquake Damaged Buildings from Post-event Aerial Photographs Using Perceptual Grouping[J]. International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, 2004, 35(B3): 1-4
[8] Chen Wenkai, He Shaolin, Zhang Jingfa, et al. A Review on Methods of Extracting Earthquake Damage Information Using Remote Sensing Technology[J]. Northwestern Seismological Journal, 2008, 30(1): 88-93 (陈文凯, 何少林, 张景发, 等. 利用遥感技术提取震害信息方法的研究进展[J]. 西北地震学报, 2008, 30(1): 88-93)
[9] Duan F, Gong H, Zhao W. Collapsed Houses Automatic Identification Based on Texture Changes of Post-Earthquake Aerial Remote Sensing Image[C]. The 18th IEEE International Conference on Geoinformatics, Beijing, China, 2010
[10] Sumer E, Turker M. Building Damage Detection from Post-Earthquake Aerial Imagery Using Buil- ding Grey-Value and Gradient Orientation Analyses[C]. The 2nd IEEE International Conference on Recent Advances in Space Technologies, Istanbul, Turkey, 2005
[11] Li Zuchuan, Ma Jianwen, Zhang Rui, et al. Extracting Damaged Buildings Information Automatically Based on Textural and Morphological Features[J]. Geomatics and Information Science of Wuhan University, 2010, 35(4): 446-450 (李祖传, 马建文, 张睿, 等. 利用融合纹理与形态特征进行地震倒塌房屋信息自动提取[J]. 武汉大学学报·信息科学版, 2010, 35(4): 446-450)
[12] Vu T T, Ban Y. Context-based Mapping of Damaged Buildings from High-Resolution Optical Satellite Images[J]. International Journal of Remote Sensing, 2010, 31(13): 3 411-3 425
[13] Yamazaki F, Vu T T, Matsuoka M. Context-based Detection of Post-Disaster Damaged Buildings in Urban Areas from Satellite Images[C]. The 4th IEEE Urban Remote Sensing Joint Event, Paris, France,2007
[14] Dou Aixia. Study on Seismic Damage Change Detecting Technique Based on Remote Sensing Images[D]. Qingdao: Shandong University of Science and Technology, 2003 (窦爱霞. 震害遥感图像变化检测技术研究[D]. 青岛: 山东科技大学, 2003)
[15] Anselin L. Local Indicators of Spatial Association-LISA[J]. Geographical Analysis, 1995, 27(2): 93-115
[16] Zhu Zhongzheng, Su Wei. The Analysis of the Classification of SPOT 5 Image Based on Local Spatial Statistics[J]. Journal of Remote Sensing, 2011, 15(5):957-972 (朱钟正, 苏伟. 基于局部空间统计分析的 SPOT 5 影像分类[J]. 遥感学报, 2011, 15(5): 957-972)
[17] Tu Jihui, Sui Haigang, Feng Wenqing, et al. Detection of Damaged Areas Based on Visual Bag-of-Words Model from Aerial Remove Sensing Images[J]. Geomatics and Information Science of Wuhan University, 2018, doi:10.13203/j.whugis20150665 (涂继辉, 眭海刚, 冯文卿, 等. 利用词袋模型检测建筑物顶面损毁区域[J]. 武汉大学学报·信息科学版, 2018, doi:10.13203/j.whugis20150665)
[18] Schweier C, Markus M. Classification of Collapsed Buildings for Fast Damage and Loss Assessment[J].Bulletin of Earthquake Engineering, 2006, 4(2): 177-192
[19] Zhang Bin, Ma Guorui, Yu Jian, et al. Change Detection of Low Buildings After Earthquake Using Very-High Resolution Pre- event Optical and Post-event SAR Images[J]. Geomatics and Information Science of Wuhan University, 2014, 39(2): 239-243 (张斌, 马国锐, 余健, 等. 利用灾前光学影像和灾后VHR SAR影像对地震低层建筑物进行变化检测[J]. 武汉大学学报·信息科学版, 2014, 39(2): 239-243)
[20] Tu Jihui, Sui Haigang, Lv Ruipeng, et al. Building Facade Damage Detection Based on the Gini Index from Oblique Aerial Images[J]. Geomatics and Information Science of Wuhan University, 2017, doi:10.13203/j.whugis20150376(涂继辉, 眭海刚, 吕枘蓬, 等. 基于基尼系数的倾斜航空影像中建筑物立面损毁检测[J]. 武汉大学学报·信息科学版, 2017, doi:10.13203/j.whugis20150376)
[2] Sui Haigang, Hua Feng, Fan Yida, et al. Road Damage Extraction from High-Resolution SAR Image Road Based on GIS Data and Bayes Network[J]. Geomatics and Information Science of Wuhan University, 2016, 41(5): 578-583 (眭海刚, 华凤, 范一大, 等. 利用GIS与贝叶斯网络进行高分辨率SAR影像道路损毁信息提取[J]. 武汉大学学报\5信息科学版, 2016, 41(5): 578-583)
[3] Tong X, Lin X, Feng T, et al. Use of Shadows for Detection of Earthquake-Induced Collapsed Buil- dings in High-Resolution Satellite Imagery[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2013, 79(5): 53-67
[4] Turker M, San B T. Detection of Collapsed Buil- dings Caused by the 1999 Izmit, Turkey Earthquake Through Digital Analysis of Post-Event Aerial Photographs[J]. International Journal of Remote Sensing, 2004, 25(21): 4 701-4 714
[5] Iwasaki Y, Yamazaki F. Detection of Building Collapse from the Shadow Lengths in Optical Satellite Images[C].The 32nd Asian Conference on Remote Sensing, Taipei, China, 2011
[6] Turker M, Sumer E. Building-based Damage Detection due to Earthquake Using the Watershed Segmentation of the Post-Event Aerial Images[J]. International Journal of Remote Sensing, 2008, 29(11): 3 073-3 089
[7] Guler M A, Turker M. Detection of the Earthquake Damaged Buildings from Post-event Aerial Photographs Using Perceptual Grouping[J]. International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, 2004, 35(B3): 1-4
[8] Chen Wenkai, He Shaolin, Zhang Jingfa, et al. A Review on Methods of Extracting Earthquake Damage Information Using Remote Sensing Technology[J]. Northwestern Seismological Journal, 2008, 30(1): 88-93 (陈文凯, 何少林, 张景发, 等. 利用遥感技术提取震害信息方法的研究进展[J]. 西北地震学报, 2008, 30(1): 88-93)
[9] Duan F, Gong H, Zhao W. Collapsed Houses Automatic Identification Based on Texture Changes of Post-Earthquake Aerial Remote Sensing Image[C]. The 18th IEEE International Conference on Geoinformatics, Beijing, China, 2010
[10] Sumer E, Turker M. Building Damage Detection from Post-Earthquake Aerial Imagery Using Buil- ding Grey-Value and Gradient Orientation Analyses[C]. The 2nd IEEE International Conference on Recent Advances in Space Technologies, Istanbul, Turkey, 2005
[11] Li Zuchuan, Ma Jianwen, Zhang Rui, et al. Extracting Damaged Buildings Information Automatically Based on Textural and Morphological Features[J]. Geomatics and Information Science of Wuhan University, 2010, 35(4): 446-450 (李祖传, 马建文, 张睿, 等. 利用融合纹理与形态特征进行地震倒塌房屋信息自动提取[J]. 武汉大学学报·信息科学版, 2010, 35(4): 446-450)
[12] Vu T T, Ban Y. Context-based Mapping of Damaged Buildings from High-Resolution Optical Satellite Images[J]. International Journal of Remote Sensing, 2010, 31(13): 3 411-3 425
[13] Yamazaki F, Vu T T, Matsuoka M. Context-based Detection of Post-Disaster Damaged Buildings in Urban Areas from Satellite Images[C]. The 4th IEEE Urban Remote Sensing Joint Event, Paris, France,2007
[14] Dou Aixia. Study on Seismic Damage Change Detecting Technique Based on Remote Sensing Images[D]. Qingdao: Shandong University of Science and Technology, 2003 (窦爱霞. 震害遥感图像变化检测技术研究[D]. 青岛: 山东科技大学, 2003)
[15] Anselin L. Local Indicators of Spatial Association-LISA[J]. Geographical Analysis, 1995, 27(2): 93-115
[16] Zhu Zhongzheng, Su Wei. The Analysis of the Classification of SPOT 5 Image Based on Local Spatial Statistics[J]. Journal of Remote Sensing, 2011, 15(5):957-972 (朱钟正, 苏伟. 基于局部空间统计分析的 SPOT 5 影像分类[J]. 遥感学报, 2011, 15(5): 957-972)
[17] Tu Jihui, Sui Haigang, Feng Wenqing, et al. Detection of Damaged Areas Based on Visual Bag-of-Words Model from Aerial Remove Sensing Images[J]. Geomatics and Information Science of Wuhan University, 2018, doi:10.13203/j.whugis20150665 (涂继辉, 眭海刚, 冯文卿, 等. 利用词袋模型检测建筑物顶面损毁区域[J]. 武汉大学学报·信息科学版, 2018, doi:10.13203/j.whugis20150665)
[18] Schweier C, Markus M. Classification of Collapsed Buildings for Fast Damage and Loss Assessment[J].Bulletin of Earthquake Engineering, 2006, 4(2): 177-192
[19] Zhang Bin, Ma Guorui, Yu Jian, et al. Change Detection of Low Buildings After Earthquake Using Very-High Resolution Pre- event Optical and Post-event SAR Images[J]. Geomatics and Information Science of Wuhan University, 2014, 39(2): 239-243 (张斌, 马国锐, 余健, 等. 利用灾前光学影像和灾后VHR SAR影像对地震低层建筑物进行变化检测[J]. 武汉大学学报·信息科学版, 2014, 39(2): 239-243)
[20] Tu Jihui, Sui Haigang, Lv Ruipeng, et al. Building Facade Damage Detection Based on the Gini Index from Oblique Aerial Images[J]. Geomatics and Information Science of Wuhan University, 2017, doi:10.13203/j.whugis20150376(涂继辉, 眭海刚, 吕枘蓬, 等. 基于基尼系数的倾斜航空影像中建筑物立面损毁检测[J]. 武汉大学学报·信息科学版, 2017, doi:10.13203/j.whugis20150376)