基于Sentinel-2影像的喀斯特地区土地利用信息提取  

Extraction of land use information in karst areas based on Sentinel-2 images

作　　者：龙紫微 汪泓[1] 贾煜 吴永俊 彭俊杰 LONG Ziwei;WANG Hong;JIA Yu;WU Yongjun;PENG Junjie(Mining College,Guizhou University,Guiyang,Guizhou 550000,China)

机构地区：[1]贵州大学矿业学院,贵州贵阳550000

出　　处：《中国岩溶》2024年第3期672-683,共12页Carsologica Sinica

基　　金：国家自然科学基金项目(41901225);河流水质对岩溶山地坡面景观变化响应的时空模拟。

摘　　要：针对喀斯特地区分类尺度难以确定,特征数量维数过高,分类精度较低的问题,文章提出了通过联合评价确定最优分割尺度、ReliefF算法对先验特征数据集进行优选,使用分层掩膜的策略,利用随机森林算法完成分类的方法。并以贵阳西南喀斯特地区为研究区,首先使用同质性与Moran's I联合评价的方法确定最优分割尺度为80,通过ReliefF算法优选出重要性较高的15个特征;在此基础上,通过对比试验验证了随机森林算法的优越性;以2020年Sentinel-2影像为实验数据设计3种面向对象分类方案。结果表明,经最优尺度计算、特征优选和分层掩膜的分类方法结果精度最高,分类总体精度、Kappa系数、AD、QD分别达到0.886、0.849、0.092、0.022。最后将该方法应用于2023年影像,分类总体精度、Kappa系数、AD、QD分别达到0.868、0.825、0.106、0.026。证明了该方法在喀斯特地区土地利用信息提取方面的优越性和适用性。Accurate land use information is the foundation of land management.Remote sensing data,characterized by its ease of acquisition,low cost,and high efficiency,has been widely used by scholars at home and abroad in the research of land use classification in combination with machine learning algorithms.Karst landforms are widely distributed in Southwest China.This region is fragile in ecological environment,due to its rugged terrain,large surface undulations,fragmented distribution of land plots.In addition,because of the long-term influence of its topography,the level of land use in the region is relatively low,and its economic development remains sluggish.Although extraction of accurate land use information is crucial for land resource management and planning in karst areas,the complex terrain and fragmented distribution of land plots in karst areas pose challenges for the extraction.Therefore,building on previous research,this study selected the southwestern part of Guiyang City,Guizhou Province-an area with karst landform characterized by complex terrain distribution and fragmented land plots-as a study area.With the use of Sentinel-2 satellite imagery as the basic data,the optimal object-oriented segmentation scale was calculated.The ReliefF algorithm was utilized to select features to input into the random forest algorithm,and land covers obtained from remote sensing images in different years were classified based on stratified classification.This study proposed a method that determined the optimal segmentation scale through joint evaluation,selected features from the prior feature dataset by random forest algorithm,and carried out classification with the use of ReliefF algorithm and a stratified masking strategy.Firstly,the optimal segmentation scale was determined as 80 by a joint evaluation with the combination of homogeneity and Moran's I.Subsequently,the ReliefF algorithm was employed to rank the importance of the initial features,with the top 15 significant features being selected.On this basis,the superi

关 键 词：喀斯特地区 随机森林 Relief F 多尺度分割 分层策略 

分 类 号：TP751[自动化与计算机技术—检测技术与自动化装置] P237[自动化与计算机技术—控制科学与工程]