遥感分类方法在建筑物震害提取中的应用(以玉树地震为例)
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摘要
建筑物损毁情况是地震灾害评估的一项重要指标,利用遥感技术快速提取震后建筑物震害信息,对科学指导地震应急救援工作具有重要意义。利用2010年4月14日青海玉树7.1级地震前后玉树县结古镇团结村高分辨率遥感影像,结合像素光谱和空间特性的纹理、结构等多源信息,基于支持向量机(SVM)方法,对地震前后建筑物信息进行分类提取,变化检测出建筑物损毁情况,并与面向对象多源信息复合的模糊分类法的分类精度、提取效率进行对比分析。研究结果表明,多源数据复合的SVM影像分类方法能够有效解决模糊分类影像破碎问题,地震前后两实相影像分类总精度达到77.53%和73.56%,提高了建筑物震害信息提取精度。
The collapse of building is a critical measurement on the earthquake hazard assessment.Using remote sensing technology to rapidly extracte earthquake building damages,and scientifically guiding earthquake emergency work has important significance.In this paper,it is illustrated how to extracte building integrality information with SVM method,using the remote sensing images of unity village before and after 7.1 earthquake in Yushu county,Qinghai on April 14,2010,and combining with multi-source data of pixel spectra and spatial feature of texture and structure,then detect the building damages and compare the accuracy of image classification and the extracting efficiency based on fuzzy method with multi-source data.This shows that the image classification based on SVM method with multi-source data can solve the image classification fragmentation which is based on the fuzzy method.The total accuracy of classification reached 77.53%and 73.56%using the images of before and after earthquake.The method can improve the extracting accuracy of building damageinformation.
The collapse of building is a critical measurement on the earthquake hazard assessment.Using remote sensing technology to rapidly extracte earthquake building damages,and scientifically guiding earthquake emergency work has important significance.In this paper,it is illustrated how to extracte building integrality information with SVM method,using the remote sensing images of unity village before and after 7.1 earthquake in Yushu county,Qinghai on April 14,2010,and combining with multi-source data of pixel spectra and spatial feature of texture and structure,then detect the building damages and compare the accuracy of image classification and the extracting efficiency based on fuzzy method with multi-source data.This shows that the image classification based on SVM method with multi-source data can solve the image classification fragmentation which is based on the fuzzy method.The total accuracy of classification reached 77.53%and 73.56%using the images of before and after earthquake.The method can improve the extracting accuracy of building damageinformation.
引文
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陈文凯.面向震害评估的遥感应用技术研究[D].中国地震局兰州地震研究所,2007:81-87.
杜培军.基于支持向量机的高光谱遥感分类进展[J].测绘通报,2006,(12):37-40.
窦爱霞.震害遥感图像变化检测技术研究[D].山东科技大学,2003:68-73.
李德江,花向红,李涛,朱成林.基于支持向量机的建筑物沉降预测模型研究[J].测绘工程,2009,(3):33-37.
苏原,王彬彬,赵勇强,吴晟.基于支持向量机的一般建筑震害预测方法研究[J].华中科技大学学报,2010,(4):81-85.
田新光.面向对象高分辨率遥感影像信息提取[D].北京:中国测绘科学研究院,2007:110-116.
王岩.面向对象图像处理方法在遥感震害提取中的应用研究[D].北京:中国地震局地震预测研究所,2009:88-93.
王晓青,魏成阶,苗崇刚.震害遥感快速提取研究:以2003年2月24日新疆巴楚-伽师6.8级地震为例[J].地学前缘,2003,10(8):285 291.
王继,徐扬,李自红,张大卫,等.利用GIS进行山西地区的震害快速预测[J].地震地磁观测与研究,2002,23(3):65-70.
杨培玉.基于纹理特征的建筑物震害信息提取方法研究[J].交通科技与经济,2013,15(6):118-121.
余先川,安卫杰,贺辉.基于面向对象的无监督分类的遥感影像自动分类方法[J].地球物理学进展,2012,27(2):35-41.
张敏,杨武年,黄瑾,等.汶川地震区航空影像地质灾害自动识别技术[J].地理与地理信息科学,2008,24(6):42-45.
张峰,薛艳丽,李英成,丁晓波.基于SVM的多源遥感影像面向对象建筑物提取方法[J].国土资源遥感,2008,20(2):27-29.
Hajime Mitomi,Fumio Yamazaki,Masashi Matsuoka.Automated detection of building damaged due to recent earthquakes using aerial television images//Proceedings of the 21st Asian Conference on Remote Sensing[C].Asian Conference on Remote Sensing,Taiwan of Chna:Taipei,2010:25—29.
Miguel Estrada,Fumio Yamazaki.Use of landsat images for the identification of damage due to the 2009 Kocaeli,Turkey Earthquake//Proceedings of the 21st Asian Conference on Remote Sensing[C].Asian Conference on Remote Sensing,Taiwan of Chna,Taipei,2010:12-16.
Vapnik V,Golowich S,Smola A.Support Vector Method for Function Approximation,Regression Estimation,and Signal Processing[C].In Advances in Neural Information Processing Systems 9,1996,Forthcoming.
Zhang Q,Wang J,Gong P et al.Study of Urban Spatial Patterns from SPOT Panchromatic Imagery Using Textural Analysis[J].International Journal of Remote Sensing,2003,24(21):4137-4160.
Zadeh L A.Fuzzy sets[J].Inf and Control,1965,8:338-353.
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