摘要
滑坡是自然界频繁发生的地质灾害,会对人民的生命造成威胁,并带来巨大的财产损失。因此,高效准确地进行滑坡提取对快速制定应急救灾方案、减少损失有着重要的意义。目前滑坡提取研究大多针对单个或少许事件,且背景地物比较单一。研究采用高分辨率遥感影像,针对复杂背景地物条件下的多起滑坡构建提取模型,并对其进行精度验证。采用马尔科夫随机场最小化能量方程结果作为滑坡提取特征,并用于滑坡提取模型构建,与目前滑坡提取研究中常用的特征相比较,验证这一特征对于滑坡提取的有效性。选用多时相的PLANET 3 m分辨率遥感影像,对2018年9月6日北海道地震引发的多起滑坡进行提取。结果表明:本研究提出的特征运用于滑坡提取中,可以提高提取精度2%,在滑坡提取的完整性上得到一定提升,为在大区域范围内的滑坡精确提取提供帮助。
Landslides represent a prevalent geological hazard with serious consequences for the safety of human lives and properties.Therefore,the efficient and accurate extraction of landslides is of great significance for the timely development of emergency response plans aimed at reducing losses.Current studies typically focus on single or a few events,often under relatively simple background conditions.To address these limitations,we propose using high-resolution remote sensing images to extract multiple landslides under complex background conditions,with the precision of the approach being verified.Specifically,we construct a landslide extraction model that utilizes the result of the minimization of energy equation using Markov random field as a feature.To evaluate the effectiveness of the model,we compare it with features commonly used in landslide extraction research.We select multi-temporal Planet 3M resolution remote sensing images to extract landslides in Hokkaido on September 6,2018.Our results demonstrate that the proposed feature improves the accuracy of landslide extraction by 2%,while also improving the integrity of the extraction to a certain extent.This approach offers valuable assistance for accurately extracting landslides in large areas.
作者
高梦洁
陈方
王雷
杨阿强
于博
GAO Mengjie;YU Bo;WANG Lei;YANG Aqiang;CHEN Fang(Key Laboratory of Digital Earth Science,Aerospace Information Research Institute,Chinese Academy of Sciences,No.9 Dengzhuang South Road,Beijing 100094,China;International Research Center of Big Data for Sustainable Development Goals,Beijing 100094,China;University of Chinese Academy of Sciences,Beijing 100049,China)
出处
《遥感技术与应用》
CSCD
北大核心
2023年第5期1180-1191,共12页
Remote Sensing Technology and Application
基金
中国—东盟地球大数据平台与应用示范项目(桂科AA20302022)。
关键词
滑坡提取
随机森林
变化检测
马尔科夫随机场
Landslide detection
Random forest
Change detection
Markov random field
