摘要
以新疆库车市东部绿洲-荒漠过渡带为研究对象,利用GF-2号遥感影像为主要数据源,在野外调查的基础上,采用基于像元的监督分类和分层次多尺度分割的面向对象分类方法对研究区植被信息进行准确识别。结果表明:1)监督分类与面向对象的分类结果大体一致,两者的总体分类精度均可达到94%以上,Kappa系数大于0.93,都体现出了较高的分类精度;2)与监督分类相比,面向对象的分类方法在总体分类精度上提升了3.79%,Kappa系数提高了0.032,具有更好的分类效果和分类精度。通过确定最优尺度分割,面向对象的分类方法可更为准确地提取研究区植被信息,为合理评价区域土地荒漠化状况提供科学依据。
Taking the oasis-desert transition zone in the eastern part of Kuqa City,Xinjiang as the research object and using GF2 remote sensing image as the main data source,on the basis of field investigation,supervised classification based on pixel and object oriented classification based on hierarchical multi-scale segmentation were used to accurately identify the vegetation information in the study area.The results showed that:1)The results of supervised classification and object-oriented classification were roughly the same.The overall classification accuracy rates of both methods could reach more than 94%,and the Kappa coefficient was greater than 0.93,both of which reflect higher classification accuracy.2)Compared with supervised classification,the object-oriented classification method improved the overall classification accuracy by 3.79%,and the Kappa coefficient increased by 0.032,which had a better classification effect and classification accuracy.By determining the optimal scale segmentation,the object-oriented classification method can more accurately extract vegetation information in the study area,and provide a scientific basis for the reasonable evaluation of the regional land desertification status.
作者
张殿岱
王雪梅
ZHANG Diandai;WANG Xuemei(College of Geography Science and Tourism,Xinjiang Normal University,Urumqi 830054,China;Xinjiang Uygur Autonomous Region Key Laboratory “Xinjiang Laboratory of Lake Environment and Resources in Arid Zone”,Urumqi 830054,China)
出处
《林业资源管理》
北大核心
2021年第3期108-113,共6页
Forest Resources Management
基金
新疆维吾尔自治区重点实验室招标课题“塔里木盆地北缘植被地上生物量遥感估测研究”(XJNUSYS2019A14)
国家自然科学基金“塔里木盆地北缘绿洲-荒漠过渡带植被对土壤盐渍化的响应研究”(41561051)。
关键词
高分二号遥感影像
监督分类
面向对象分类
深度学习
多尺度分割
high-resolution No.2 remote sensing image
supervised classification
object-oriented classification
deep learning
multi-scale segmentation
