摘要
快速准确地获取水体信息对于水资源管理利用以及灾害防治具有重要意义。利用不同分类方法探讨多种数据源在不同天气场景下水体提取的最优技术,结合Sentinel和Landsat系列数据,对红碱淖1973—2018年湖泊面积变化进行分析。结果表明:在无云的情况下,使用Sentinel-2结合最大似然分类方法提取效果最好,精度为99.30%;Sentinel-1利用面向对象分类精度最高,总体精度为95.70%,最大似然分类法次之;融合数据利用最大似然法精度最高,比Sentinel-1数据总体精度提高了2.00%;有云情况下,Sentinel-1数据通过融合同期Sentinel-2光学数据能有效提取被云覆盖的区域,其提取水体精度比仅使用Sentinel-1雷达数据精度提高了2.50%。
Extraction water body information quickly and accurately has great significant to the management and utilization of water resources and the prevention and control of disasters.This study used different classification methods to explore multi-source data sources in different weather scenarios.The results show that,in the case of no cloud,for Sentinel-2 data using the maximum likelihood classification method is the best method to extract water body,with an accuracy of 99.3%;for Sentinel-1 synthetic aperture radar data using the object-oriented classification method is the best method to extract water body,with overall accuracy of 95.7%,the maximum likelihood classification is the next;for fusing data using the maximum likelihood classification method is the highest and its overall accuracy increased by 2%,compared with the Sentinel-1 data.In the case of cloud,by fusing the Sentinel-2 data over the same period,the accuracy of water extraction has been improved by 2.5%compared with using only the sentinel-1 data.
作者
白翠
向洋
邱春霞
赵贝贝
张巧玲
BAI Cui;XIANG Yang;QIU Chunxia;ZHAO Beibei;ZHANG Qiaoling(College of Geomatics,Xi’an University of Science and Technology,Xi’an 710054,China)
出处
《人民黄河》
CAS
北大核心
2021年第7期78-83,共6页
Yellow River
基金
国家自然科学基金资助项目(41701069)
西安科技大学博士启动基金项目(2017QDJ039)。
关键词
光学数据
雷达数据
水体提取
影像融合
红碱淖
optical data
radar data
water extraction
images fusing
Hongjian Lake
