摘要
为实现江河类狭长型水体信息的精细化提取,利用GF-1卫星数据,采用支持向量机和目视解译相结合的方法对三峡库区及重庆市水体信息进行了精细化提取。使用总体分类精度、Kappa系数、错分误差、漏分误差、制图精度和用户精度等指标对库区水体信息粗提取结果进行验证分析。结果表明:4个试验区水体提取的总体分类精度均超过90%,除试验区4的Kappa系数为0.8841以外,其余试验区均超过0.9,提取精度较高。结合目视解译的方法,在粗提取结果的基础上对各问题进行精细化处理,得到精度高、完整性好的三峡库区以及重庆市水体信息数据,可为后续该区域的精细化遥感业务开展提供有效资料。
In order to extract the water body information of the long and narrow water area,based on the data of GF-1,and by combining the support vector machine and the visual interpretation,the water body information of the Three Gorges Reservoir and Chongqing City was extracted in this paper.Then,the overall classification accuracy,Kappa coefficient,commission errors,omission errors?,the drawing accuracy and the user’s accuracy were used to validate the results of the rough extraction of water body in the reservoir area.The results showed that the overall classification accuracy of the information extraction in the four experimental areas is more than 90%,except that the Kappa coefficient of the test area 4 is 0.8841,the rest of the test area is more than 0.9,and the extraction accuracy is high.According to the method of visual interpretation on the basis of the rough extraction,the information is refined.The water body information of the Three Gorges Reservoir and Chongqing are obtained with high accuracy and good integrity,and the effective data can provide reference for the fine remote sensing service of this region.
作者
张德军
杨世琦
王永前
郑伟
ZHANG Dejun;YANG Shiqi;WANG Yongqian;ZHENG Wei(Chongqing Institute of Meteorological Sciences,Chongqing 401147,China;College of Environmental and Resource Science,Chengdu University of Information Technology,Chengdu 610225,China;National Satellite Meteorological Center,Beijing 100081,China)
出处
《人民长江》
北大核心
2019年第9期233-239,共7页
Yangtze River
基金
国家自然科学基金项目“被动微波遥感洪涝信息定量反演及超分辨率成图方法研究”(41571425)
重庆市气象局智慧气象技术创新团队项目“基于高分一号数据的重庆地物提取初探”(ZHCXTD-201826)
关键词
水体信息
精细化提取
GF-1
支持向量机
三峡库区
water body information
refined extraction
GF-1 satellite
support vector machine
high resolution satellite
Three Gorges reservoir area
