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

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0.820 4,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。

Comparison between Landsat-8 and GF-1 Images in Land Use Data Extraction-Taking Xianning City as an Example

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.