利用自适应增强检测地震破坏程度

Mona Peyk Herfeh, A. Shahbahrami, Farshad Parhizkar Miandehi
{"title":"利用自适应增强检测地震破坏程度","authors":"Mona Peyk Herfeh, A. Shahbahrami, Farshad Parhizkar Miandehi","doi":"10.1109/IRANIANMVIP.2013.6779989","DOIUrl":null,"url":null,"abstract":"When an earthquake happens, the image-based techniques are influential tools for detection and classification of damaged buildings. Obtaining precise and exhaustive information about the condition and state of damaged buildings after an earthquake is basis of disaster management. Today's using satellite imageries such Quickbird is becoming more significant data for disaster management. In this paper, a method for detecting and classifying of damaged buildings using satellite imageries and digital map is proposed. In this method after extracting buildings position from digital map, they are located in the pre-event and post-event images of Bam earthquake. After generating features, genetic algorithm applied for obtaining optimal features. For classification, Adaptive boosting is used and compared with neural networks. Experimental results show that total accuracy of adaptive boosting for detecting and classifying of collapsed buildings is about 84 percent.","PeriodicalId":297204,"journal":{"name":"2013 8th Iranian Conference on Machine Vision and Image Processing (MVIP)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2013-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":"{\"title\":\"Detecting earthquake damage levels using adaptive boosting\",\"authors\":\"Mona Peyk Herfeh, A. Shahbahrami, Farshad Parhizkar Miandehi\",\"doi\":\"10.1109/IRANIANMVIP.2013.6779989\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"When an earthquake happens, the image-based techniques are influential tools for detection and classification of damaged buildings. Obtaining precise and exhaustive information about the condition and state of damaged buildings after an earthquake is basis of disaster management. Today's using satellite imageries such Quickbird is becoming more significant data for disaster management. In this paper, a method for detecting and classifying of damaged buildings using satellite imageries and digital map is proposed. In this method after extracting buildings position from digital map, they are located in the pre-event and post-event images of Bam earthquake. After generating features, genetic algorithm applied for obtaining optimal features. For classification, Adaptive boosting is used and compared with neural networks. Experimental results show that total accuracy of adaptive boosting for detecting and classifying of collapsed buildings is about 84 percent.\",\"PeriodicalId\":297204,\"journal\":{\"name\":\"2013 8th Iranian Conference on Machine Vision and Image Processing (MVIP)\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2013-09-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"5\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2013 8th Iranian Conference on Machine Vision and Image Processing (MVIP)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/IRANIANMVIP.2013.6779989\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2013 8th Iranian Conference on Machine Vision and Image Processing (MVIP)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IRANIANMVIP.2013.6779989","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 5

摘要

当地震发生时,基于图像的技术是检测和分类受损建筑物的重要工具。在地震发生后,获得有关受损建筑物状况和状态的准确而详尽的信息是灾害管理的基础。今天使用卫星图像,如Quickbird,正在成为灾害管理的更重要的数据。本文提出了一种利用卫星图像和数字地图对受损建筑进行检测和分类的方法。该方法从数字地图中提取建筑物位置后,将建筑物定位在巴姆地震的震前和震后图像中。在生成特征后,应用遗传算法获得最优特征。在分类方面,采用自适应增强方法,并与神经网络进行了比较。实验结果表明,自适应增强对建筑物倒塌检测和分类的总准确率约为84%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Detecting earthquake damage levels using adaptive boosting
When an earthquake happens, the image-based techniques are influential tools for detection and classification of damaged buildings. Obtaining precise and exhaustive information about the condition and state of damaged buildings after an earthquake is basis of disaster management. Today's using satellite imageries such Quickbird is becoming more significant data for disaster management. In this paper, a method for detecting and classifying of damaged buildings using satellite imageries and digital map is proposed. In this method after extracting buildings position from digital map, they are located in the pre-event and post-event images of Bam earthquake. After generating features, genetic algorithm applied for obtaining optimal features. For classification, Adaptive boosting is used and compared with neural networks. Experimental results show that total accuracy of adaptive boosting for detecting and classifying of collapsed buildings is about 84 percent.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Automated lung CT image segmentation using kernel mean shift analysis A simple and efficient approach for 3D model decomposition MRI image reconstruction via new K-space sampling scheme based on separable transform Fusion of SPECT and MRI images using back and fore ground information Real time occlusion handling using Kalman Filter and mean-shift
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1