基于震后高分辨率卫星遥感影像的建筑物瓦砾快速提取方法
摘要
利用震后高分辨率卫星遥感影像提取建筑物损毁空间分布和破坏程度信息,对于地震灾情评估具有重要作用.本文以2010年海地地震巨灾为例,选用震后高分辨率卫星遥感影像Geoeye-1为数据源,在分析建筑物瓦砾可分离性的基础之上,利用监督分类方法提取损毁建筑物的瓦砾.结果表明,在震后高分辨率卫星遥感影像中,瓦砾是建筑物损毁的明显震害标志;瓦砾的生产者精度为87.23%,大于总体分类精度63.14%;瓦砾的Kappa系数为0.62,高于总体Kappa系数0.54.研究表明,基于瓦砾纹理特征的遥感信息提取方法能够从震后复杂的城市地物类型中识别出大部分瓦砾,该方法得到的结果可以应用于地震灾情应急评估,辅助应急救援等.
Using post-earthquake high-resolution satellite imagery to detect the spatial distribution and extent of building damage can play an important role in the assessment of earthquake disaster loss. The paper takes Haiti Earthquake catastrophe in 2010 as an example,and uses Geoeye-1 very high resolution satellite imagery to extract the rubble of damaged building based on supervised classification method,after analyzing the detachability of building rubble. The results show the rubble is a clear sign of building damage in post-earthquake high-resolution satellite imagery. In the precision evaluation,the producer accuracy and Kappa coefficient of rubble are 87. 23% and 0. 62 respectively,higher than the total classification accuracy( 63. 14%)and the whole Kappa coefficient( 0. 54). Most of rubble could be extracted from post-earthquake complex urban building type based upon rubble texture. The method used in the paper could be applied in earthquake disaster emergency assessment,and could assist in emergency rescue as well.
Using post-earthquake high-resolution satellite imagery to detect the spatial distribution and extent of building damage can play an important role in the assessment of earthquake disaster loss. The paper takes Haiti Earthquake catastrophe in 2010 as an example,and uses Geoeye-1 very high resolution satellite imagery to extract the rubble of damaged building based on supervised classification method,after analyzing the detachability of building rubble. The results show the rubble is a clear sign of building damage in post-earthquake high-resolution satellite imagery. In the precision evaluation,the producer accuracy and Kappa coefficient of rubble are 87. 23% and 0. 62 respectively,higher than the total classification accuracy( 63. 14%)and the whole Kappa coefficient( 0. 54). Most of rubble could be extracted from post-earthquake complex urban building type based upon rubble texture. The method used in the paper could be applied in earthquake disaster emergency assessment,and could assist in emergency rescue as well.
引文
[1]Saito,K.High-resolution optical satellite images for post-earthquake damage assessment[D].Ph D Dissertation,University of Cambridge,2008
[2]董燕生.基于震后高分辨率光学遥感影像的城市建筑物震害信息提取方法研究[D].北京师范大学博士研究学位论文,2010Dong Yansheng.High-resolution optical satellite imagery for post-earthquake building damage assessment in urban area[D].Ph D Dissertation,Beijing Normal University,2010
[3]Coburn A,Spence R.Earthquake protection[M].2nd edition.New York:John Wiley&Sons,2002:420
[4]Saito K,Spence R J,Going C,et al.Using high-resolution satellite images for post-earthquake building damage assessment:A study following the 26 January 2001 Gujarat earthquake[J].Earthquake Spectra,2004,20:145-169
[5]Yano Y,Yamazaki F,Matsuoka M,et al.Building damage detection of the 2003 Bam,Iran earthquake using Quick Bird images[C].Proceedings of the 25th Asian Conference on Remote Sensing,Chiang-Mai,Thailand,2004:618-623
[6]Ogawa N,Yamazaki F.Photo-interpretation of building damage due to earthquakes using aerial photographs[C].Proceedings of the 12th World Conference on Earthquake Engineering,Auckland,New Zealand,2000:1906
[7]董燕生,潘耀忠,方伟华,等.面向对象的建筑物地震灾害提取方法研究[J].地震研究,2011,34(3):372-377Dong Yansheng,Pan Yaozhong,Fang Weihua,et al.Recognition of the earthquake-damaged building based on objectoriented technology[J].Journal of Seismological Research,2011,34(3):372-377
[8]Chica-Olmo M,Abarca-Hernandez F.Computing geostatistical image texture for remotely sensed data classification[J].Computers&Geosciences,2000,26:373-383
[9]Woodcock C E,Strahler A H,Jupp D L B.The use of variograms in remote sensing:I.Scene models and simulated images[J].Remote Sensing of Environment,1988,25(3):323-348
[10]Miranda F P,Carr J R.Automatic classification of the semiariogram texture classifier(STC)for vegetation discrimination using SIR-B data of the Guiana Shield,Northwestern Brazil[J].Remote Sensing Reviews,1994,10:155-168
[11]Chica-Olmo M,Abarca-Hernandez F.Computing geostatistical image texture for remotely sensed data classification[J].Computers&Geosciences,2000,26:373-383
[12]Lark R M.Geostatistical description of texture on an aerial photograph for discriminating classes of land cover[J].International Journal of Remote Sensing,1996,17(11):2115-2133
[13]Curran P J.The semivariogram in romte sensing:An introduction[J].Remote Sensing of Environment,1988(24):493-507
[14]马洪超,李德仁.基于空间统计学的空间数据窗口大小的确定[J].武汉大学学报(信息科学版),2001,26(1):18-23Ma Hongchao,Li Deren.Geographic window fixing and its application based on spatial statistics[J].Geomatics and Information Science of Wuhan University,2001,26(1):18-23
[15]Walker J S,Blaschke T.Object-based land-cover classification for the Phoenix metropolitan area:Optimization vs.transportability[J].International Journal of Remote Sensing,2008,29(7):2021-2040
[2]董燕生.基于震后高分辨率光学遥感影像的城市建筑物震害信息提取方法研究[D].北京师范大学博士研究学位论文,2010Dong Yansheng.High-resolution optical satellite imagery for post-earthquake building damage assessment in urban area[D].Ph D Dissertation,Beijing Normal University,2010
[3]Coburn A,Spence R.Earthquake protection[M].2nd edition.New York:John Wiley&Sons,2002:420
[4]Saito K,Spence R J,Going C,et al.Using high-resolution satellite images for post-earthquake building damage assessment:A study following the 26 January 2001 Gujarat earthquake[J].Earthquake Spectra,2004,20:145-169
[5]Yano Y,Yamazaki F,Matsuoka M,et al.Building damage detection of the 2003 Bam,Iran earthquake using Quick Bird images[C].Proceedings of the 25th Asian Conference on Remote Sensing,Chiang-Mai,Thailand,2004:618-623
[6]Ogawa N,Yamazaki F.Photo-interpretation of building damage due to earthquakes using aerial photographs[C].Proceedings of the 12th World Conference on Earthquake Engineering,Auckland,New Zealand,2000:1906
[7]董燕生,潘耀忠,方伟华,等.面向对象的建筑物地震灾害提取方法研究[J].地震研究,2011,34(3):372-377Dong Yansheng,Pan Yaozhong,Fang Weihua,et al.Recognition of the earthquake-damaged building based on objectoriented technology[J].Journal of Seismological Research,2011,34(3):372-377
[8]Chica-Olmo M,Abarca-Hernandez F.Computing geostatistical image texture for remotely sensed data classification[J].Computers&Geosciences,2000,26:373-383
[9]Woodcock C E,Strahler A H,Jupp D L B.The use of variograms in remote sensing:I.Scene models and simulated images[J].Remote Sensing of Environment,1988,25(3):323-348
[10]Miranda F P,Carr J R.Automatic classification of the semiariogram texture classifier(STC)for vegetation discrimination using SIR-B data of the Guiana Shield,Northwestern Brazil[J].Remote Sensing Reviews,1994,10:155-168
[11]Chica-Olmo M,Abarca-Hernandez F.Computing geostatistical image texture for remotely sensed data classification[J].Computers&Geosciences,2000,26:373-383
[12]Lark R M.Geostatistical description of texture on an aerial photograph for discriminating classes of land cover[J].International Journal of Remote Sensing,1996,17(11):2115-2133
[13]Curran P J.The semivariogram in romte sensing:An introduction[J].Remote Sensing of Environment,1988(24):493-507
[14]马洪超,李德仁.基于空间统计学的空间数据窗口大小的确定[J].武汉大学学报(信息科学版),2001,26(1):18-23Ma Hongchao,Li Deren.Geographic window fixing and its application based on spatial statistics[J].Geomatics and Information Science of Wuhan University,2001,26(1):18-23
[15]Walker J S,Blaschke T.Object-based land-cover classification for the Phoenix metropolitan area:Optimization vs.transportability[J].International Journal of Remote Sensing,2008,29(7):2021-2040
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