Earthquake-triggered landslide interpretation model of high resolution remote sensing imageries based on bag of visual word

Ruyue Bai , Zegen Wang , Heng Lu , Chen Chen , Xiuju Liu , Guohao Deng , Qiang He , Zhiming Ren , Bin Ding , Xin Ye
{"title":"Earthquake-triggered landslide interpretation model of high resolution remote sensing imageries based on bag of visual word","authors":"Ruyue Bai ,&nbsp;Zegen Wang ,&nbsp;Heng Lu ,&nbsp;Chen Chen ,&nbsp;Xiuju Liu ,&nbsp;Guohao Deng ,&nbsp;Qiang He ,&nbsp;Zhiming Ren ,&nbsp;Bin Ding ,&nbsp;Xin Ye","doi":"10.1016/j.eqrea.2022.100196","DOIUrl":null,"url":null,"abstract":"<div><p>Traditional visual interpretation is often inefficient due to its excessively workload professional knowledge and strong subjectivity. Therefore, building an automatic interpretation model on high spatial resolution remote sensing images is the key to the quick and efficient interpretation of earthquake-triggered landslides. Aiming at addressing this problem, a landslide interpretation model of high-resolution images based on bag of visual word (BoVW) feature was proposed. The high-resolution images were pre-processed, and then BoVW feature and support vector machine (SVM) was adopted to establish an automatic landslide interpretation model. This model was further compared with the currently widely used Histogram of Oriented Gradient(HoG) feature extraction model. In order to test the effectiveness of the method, typical landslide images were selected to construct a landslide sample library, which was subsequently utilized as the foundation for conducting an experimental study. The results show that the accuracy of landslide extraction using this method reaches as high as 89%, indicating that the method can be used for the automatic interpretation of landslides in disaster-prone areas, and has high practical value for regional disaster prevention and damage reduction.</p></div>","PeriodicalId":100384,"journal":{"name":"Earthquake Research Advances","volume":"3 2","pages":"Article 100196"},"PeriodicalIF":0.0000,"publicationDate":"2023-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Earthquake Research Advances","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2772467022000872","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

Traditional visual interpretation is often inefficient due to its excessively workload professional knowledge and strong subjectivity. Therefore, building an automatic interpretation model on high spatial resolution remote sensing images is the key to the quick and efficient interpretation of earthquake-triggered landslides. Aiming at addressing this problem, a landslide interpretation model of high-resolution images based on bag of visual word (BoVW) feature was proposed. The high-resolution images were pre-processed, and then BoVW feature and support vector machine (SVM) was adopted to establish an automatic landslide interpretation model. This model was further compared with the currently widely used Histogram of Oriented Gradient(HoG) feature extraction model. In order to test the effectiveness of the method, typical landslide images were selected to construct a landslide sample library, which was subsequently utilized as the foundation for conducting an experimental study. The results show that the accuracy of landslide extraction using this method reaches as high as 89%, indicating that the method can be used for the automatic interpretation of landslides in disaster-prone areas, and has high practical value for regional disaster prevention and damage reduction.

基于视觉字袋的高分辨率遥感影像地震诱发滑坡解译模型
传统的视觉口译由于专业知识工作量大、主观性强,往往效率低下。因此,建立高空间分辨率遥感图像的自动解释模型是快速高效解释地震诱发滑坡的关键。针对这一问题,提出了一种基于视觉词袋特征的高分辨率滑坡图像解释模型。对高分辨率图像进行预处理,然后采用BoVW特征和支持向量机(SVM)建立滑坡自动解释模型。该模型与目前广泛使用的面向梯度直方图(HoG)特征提取模型进行了比较。为了验证该方法的有效性,选取了典型的滑坡图像构建了滑坡样本库,并以此为基础进行了实验研究。结果表明,该方法提取滑坡体的准确率高达89%,可用于灾害多发区滑坡体的自动解释,对区域防灾减灾具有较高的实用价值。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
CiteScore
4.30
自引率
0.00%
发文量
0
×
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
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
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 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信