摘要
滑坡实时自动识别技术研究对于保护人民生命财产和生态安全都具有极其重要的意义,可以解决现阶段缺乏对滑坡进行及时识别而导致滑坡风险的排查与防治时效性差的问题。考虑植被覆盖指数(NDVI)的变化可作为滑坡检测的重要依据之一,文章模型结合NDVI变化检测技术、自动阈值选取算法和形态学技术实现滑坡的实时、自动识别。与现阶段已有研究算法相比,增加了滑坡自动识别过程中一些重要参数(如:NDVI、山体阴影等)。自适应设置的自动阈值选取算法,减少了人工参与,在保证较高识别准确率的同时显著增强其时效性。文章基于两幅光学影像,以北京市门头沟区某块区域为研究区,对该地区2021年9月7日—2022年9月7日的滑坡进行实时、自动识别,以人工目视解译的结果作为正确标准,将文章的识别结果与其进行精度验证,滑坡检测率达到92.31%,证明了该方法用于检测滑坡的准确性和高效性。最后将该方法应用于都江堰市中部,进一步证明了该方法的有效性和泛化能力。
The research on real-time automatic identification technology for landslides is of great significance for protecting people’s lives,property,and ecological safety.It can solve the problem of poor timeliness in landslide risk investigation and prevention due to the lack of timely identification of landslides at present.Considering the changes of vegetation coverage index(NDVI)as one of the important criteria for landslide detection,the article combines NDVI change detection technology,automatic threshold selection algorithm,and morphological technology to achieve real-time and automatic recognition of landslides.Compared with existing research algorithms,Compared with existing research algorithms,it adds some important parameters in the automatic landslide recognition process(such as NDVI,mountain shadows,etc.).The adaptive automatic threshold selection algorithm reduces manual involvement and significantly enhances its timeliness while ensuring high recognition accuracy.This article is based on two optical images and takes a certain area in Mentougou District,Beijing as the research area.Real time and automatic recognition of landslides in this area from September 7,2021 to September 7,2022 is carried out,using the results of manual visual interpretation as the correct standard.The recognition results of the article are compared with their accuracy,and the landslide detection rate reached 92.31%,proving the accuracy and high efficiency of this method for detecting landslides.Finally,the method is applied to the central part of Dujiangyan City,which further proves the effectiveness and generalization ability of the method.
作者
王辉
范硕超
黄晓胤
邱鹏
于竞哲
李进田
WANG Hui;FAN Shuochao;HUANG Xiaoyin;QIU Peng;YU Jingzhe;LI Jintian(State Grid Jibei Electric Power Co.Ltd.,Beijing 100054,China;Beijing EHV Power Transmission Company,State Grid Jibei Electric Power Co.Ltd.,Beijing 102488,China;Electric Power Research Institute,State Grid Jibei Electric Power Co.Ltd.,Beijing 100045,China;Beijing Deep Blue Space Remote Sensing Technology Co.,Ltd.,Beijing 100101,China)
出处
《航天返回与遥感》
CSCD
北大核心
2024年第1期147-160,共14页
Spacecraft Recovery & Remote Sensing
关键词
归一化植被指数
变化检测
自动阈值
形态学
滑坡实时自动识别
遥感应用
normalized vegetation index
change detection
automatic threshold
morphology
suspected landslide intelligent detection
remote sensing application
