Landsat-8与GF-1遥感影像土地利用数据提取比较——以咸宁市为例
Comparison between Landsat-8 and GF-1 Images in Land Use Data Extraction-Taking Xianning City as an Example
周霞 1刘彦文 1姜宇榕 1刘建1
作者信息
- 1. 湖北科技学院资源环境科学与工程学院,湖北咸宁437100
- 折叠
摘要
针对Landsat-8 OLI和GF-1 WFV传感器参数的特点,选择支持向量机(SVM)分类方法分别对咸宁市同一时段的Landsat-8遥感影像和GF-1遥感影像进行土地利用分类研究.结果表明,Landsat-8在耕地与林地、水域与裸地可分离性方面高于GF-1,提取的林地面积占比和耕地面积占比更接近于真实值;Landsat-8和GF-1的分类总精度分别为85.76％和88.38％,Kappa系数分别为0.807 1和0.8204,说明GF-1的分类效果好于Landsat-8;GF-1具有较高的分辨率优势,对分布零散的地物识别效果优于Landsat-8.
Abstract
According to the characteristics of Landsat-8OLI and GF-1 WFV sensor parameters,the support vector machine (SVM) classification method was used to classify Landsat-8 remote sensing images and GF-1 remote sensing images at the same time in Xianning City.The results showed that the separation of water area Landsat-8 in the cultivated land,forest land,and bare land was higher than that of GF-1,and the proportion of extracted forest land and cultivated land was closer to the real value.The classification total accuracy of Landsat-8 and GF-1 were 85.76％and 88.38％ respectively,and Kappa coefficients were 0.807 1 and 0.820 4 respectively.The classification effect of GF-1 was better than that of Landsat-8.GF-1 had higher resolution advantages,and the classification effect of the fragmented landform type was better than that of Landsat-8.
关键词
遥感影像/监督分类/可分离度/Kappa系数Key words
Remote sensing images/Supervised classification/Separability/Kappa coefficient引用本文复制引用
基金项目
湖北省教育厅人文社会科学研究青年项目(15Q217)
湖北科技学院校级科研项目(2016-18X058)
出版年
2017
国家科技期刊平台
NSTL
维普
万方数据