面向对象的无人机遥感影像区域滑坡承灾体信息提取研究  

RESEARCH ON OBJECT-ORIENTED INFORMATION EXTRACTION OF REGIONAL LANDSLIDE ELEMENTS AT RISK FROM UAV REMOTE SENSING IMAGE

作　　者：毛正君[1,2,3] 李欢 石硕杰 孙婕文 仲佳鑫 于海泳 MAO Zhengjun;LI Huan;SHI Shuojie;SUN Jiewen;ZHONG Jiaxin;YU Haiyong(College of Geology and Environment,Xi'an University of Science and Technology,Xi'an 710054,China;Geological Research Institute for Coal Green Mining,Xi'an University of Science and Technology,Xi'an 710054,China;Shaanxi Provincial Key Laboratory of Geological Support for Coal Green Exploitation,Xi'an 710054,China;Remote Sensing Surveying and Mapping Survey Insitute,Ningxia Hui Autonomous Region(Remote Sensing Center of Ningxia Hui Autonomous Region),Yinchuan 750021,China;Ningxia Hui Autonomous Region Institute of Survey and Monitoring of Land and Resources,Yinchuan 750021,China)

机构地区：[1]西安科技大学,地质与环境学院,西安710054 [2]西安科技大学,煤炭绿色开采地质研究院,西安710054 [3]陕西省煤炭绿色开发地质保障重点实验室,西安710054 [4]宁夏回族自治区遥感调查院(宁夏回族自治区遥感中心),银川750021 [5]宁夏回族自治区国土资源调查监测院,银川750021

出　　处：《工程地质学报》2025年第1期171-185,共15页Journal of Engineering Geology

基　　金：宁夏回族自治区重点研发计划项目(资助号:2020BEG03023,2022BEG03059);陕西省重点研发计划项目(资助号:2020SF-379).

摘　　要：采用无人机遥感获取低成本、高空间分辨率且实时的影像,并基于面向对象分类自动提取滑坡承灾体信息意义重大。本文以宁夏固原市彭阳县红河镇为研究区,基于无人机遥感获取分辨率为0.13 m的174 km^(2)正射影像,在分析其滑坡承灾体影像特征后,设定实验区并采用多尺度分割和单一尺度分割两种面向对象的滑坡承灾体信息提取方法,比较了基于两种尺度分割的滑坡承灾体信息提取精度和时间效率,通过方案优化实现了研究区滑坡承灾体信息提取。结果表明:(1)通过引入反映分割结果的均质性的局部方差指数,对实验区进行多尺度分割的最优分割尺度划分,通过构建光谱、范围、形状特征规则,能够依次在不同层次上提取出相应的承灾体信息;(2)在多尺度分割的基础上结合试算设置实验区单一分割尺度,承灾体信息提取的总体精度和Kappa系数虽逊于多尺度分割但相差不大,所需时间不到多尺度分割的四分之一;(3)综合考虑研究区滑坡承灾体空间分布、影像特征信息和实验区滑坡承灾体信息提取结果,确定研究区单一分割尺度优化方案。研究结果以期为区域滑坡承灾体信息提取、滑坡风险评估及风险管理提供必要参考。In this paper,Honghe Town,Pengyang County,Guyuan City,Ningxia was selected as the research area,an orthophoto with a resolution of 0.13 m and an area of 174 km^(2)was obtained by UAV.After analyzing the image features of the landslide elements at risk,the test area is set.Two object-oriented methods,multi-scale segmentation and single-scale segmentation,are used in the experimental area,and the extraction accuracy and time efficiency of the two methods are compared.Through the optimization of the scheme,the information of landslide elements at risk in the study area is extracted.The results show that:(1)By introducing the local variance index reflecting the homogeneity of the segmentation results,the optimal segmentation scale of multi-scale segmentation was carried out for the experimental area.By constructing the characteristic rules of spectrum,range and shape,the corresponding landslide elements at risk information could be extracted at different levels successively;(2)On the basis of multi-scale segmentation,a single segmentation scale was set up in the experimental area by combining trial calculation.Although the overall accuracy and Kappa coefficient of elements at risk information extraction were lower than that of multi-scale segmentation,there was little difference,and the time required was less than a quarter of that of multi-scale segmentation;(3)Considering the spatial distribution of landslide elements at risk in the study area,the image feature information and the extraction results of landslide elements at risk in the experimental area,the single segmentation scale optimization scheme of the study area was determined.The research results are expected to provide necessary references for regional landslide elements at risk information extraction,landslide risk assessment and risk management.

关 键 词：滑坡承灾体 无人机遥感 面向对象分类 尺度分割 信息提取 

分 类 号：P642.4[天文地球—工程地质学]