摘要
黄土陷穴作为黄土高原地区一种特殊的地貌类型及地质灾害,其研究对指导黄土地区水土保持与工程建设工作具有重要意义。现阶段对陷穴的研究多基于传统野外调查,该方式成本高、效率低。为此,该研究开展面向对象与卷积神经网络(Convolutional Neural Networks,CNN)相结合的黄土陷穴自动化提取方法研究,并讨论融合地形特征对CNN模型提取精度的影响。研究选取黄土陷穴发育的典型区域,基于WorldView3遥感数据与ALOS高程数据,通过莫兰指数与灰度共生矩阵熵值确定影像的分割尺度,以面向对象的方式提取黄土陷穴的光谱、形状、纹理以及地形特征,制作融合地形特征与未融合地形特征的两类训练样本,进而训练两种CNN模型对同一区域内黄土陷穴进行提取,根据精确率、召回率以及F1分数评价模型的提取精度、分析对比两种CNN模型的提取结果,并建立支持向量机(SupportVector Machine,SVM)模型与CNN模型进行比较。研究结果表明,融合地形特征进行训练的CNN模型精确率达94.62%,召回率达86.27%、F1分数达90.26%,综合提取性能最好,相较于未融合地形特征训练的CNN模型,黄土陷穴的错分量大大减少,精确率提升18.10个百分点,F1分数提升9.15个百分点;两种CNN模型F1分数均达80%以上,比SVM模型分别高出6.94个百分点,16.09个百分点,提取结果均优于SVM模型;综上,融合地形特征的CNN模型可快速、精确地提取黄土陷穴,从而为黄土地区陷穴防治工作提供支持。
A loess sinkhole is a typical geological hazard that is widely distributed in the Loess Plateau of China.However,some latent damages are difficult to detect in the natural hazard.Particularly,the serious damage caused by loess sinkholes is ever increasing,as the infrastructure rapidly developed in recent years.The resulting water and soil loss can aggravate and even trigger the occurrence,development of mudflows,collapses,and landslides.In addition,the sinkhole collapse can also seriously damage the farmland infrastructure,such as terraces,water storage facilities,and irrigation systems,as well as the infrastructures such as highways,railways,bridges,and oil and gas pipelines.Thus,it is a high demand to detect the loess sinkholes for the soil and water conservation and engineering construction in the loess regions.However,the traditional field surveys are costly and inefficient on the loess sinkholes.In this study,an automatic and object-oriented extraction of loess sinkholes was proposed to determine the influence of integrated terrain features on the extraction accuracy of the Convolutional Neural Networks(CNN) model.The study area was selected as Lanzhou City,Gansu Province in western China.The segmentation scale of the image was then determined by the Moran’s I and Gray Level Co-occurrence Matrix entropy using World View 3 remote sensing image and ALOS digital elevation model data.An object-oriented extraction was implemented to obtain the spectrum(i.e.,Mean_R,Mean_G,Mean_B,Std_R,Std_G,Std_B,Max_diff,and Brightness),shape(i.e.,Area,Asymmetry,Border_index,Border_Length,Compactness,Density,Elliptic_Fit,Length/Width,Length,Shape_index,Roundness and Width),texture(i.e.,GLCM_Homogeneity,GLCM_Contrast,GLCM_Mean,GLCM_Dissimilarity,GLCM_Entropy,and GLCM_Std),and terrain(i.e.,Mean_DEM,Mean_Slope,Mean_Hillshade,Std_DEM,Std_Slope,and Std_Hillshade) features of the loess sinkholes.Two types of training samples were constructed with/without the terrain features,and then to train two CNN models for the extraction of the
作者
苏旭
黄骁力
王春
吴复柱
江岭
Su Xu;Huang Xiaoli;Wang Chun;Wu Fuzhu;Jiang Ling(School of Earth Science and Engineering,Hebei University of Engineering,Handan 056038,China;School of Geographic Information and Tourism,Chuzhou University,Chuzhou 239000,China)
出处
《农业工程学报》
EI
CAS
CSCD
北大核心
2022年第10期102-110,共9页
Transactions of the Chinese Society of Agricultural Engineering
基金
国家自然科学基金项目(42101425、41930102、41871313)
安徽省高校科学研究项目(KJ2020A0705)
河北省自然科学基金(D2021402033)
滁州学院科研启动基金项目(2020QD44)
滁州市科技计划项目重点研究开发专项(2020ZG016)
安徽省留学回国人员创新项目(2021LCX014)。
关键词
遥感
提取
卷积神经网络
面向对象
黄土陷穴
地形特征
影像分割
remote sensing
extraction
convolutional neural networks
object-oriented
loess sinkhole
terrain features
image segmentation
