摘要
基于多时相的GF-1数据获取NDVI时序变化、NDWI和MNDVI等指数图像数据,辅以Landsat8卫星OLI影像和数字高程模型(DEM)数据,得到了不同地物在光谱、时相和形状等方面的特征;通过分析各种地物类型在这些特征上的差异和变化规律,总结出不同地物的特征提取规则,构建了一种基于GF-1数据在地物复杂地区的土地利用/覆盖分类方法,并以广州市为实验区,运用该方法、最大似然法和最小距离法进行了土地利用/覆盖分类及其精度评价.结果显示:基于GF-1数据在地物复杂地区的土地利用/覆盖分类方法的总体精度为85.86%(部分地物分类精度达到95%以上),与最大似然法及最小距离法相比,其总体精度分别提高了4.62%和12.24%,说明该方法能够更好地发挥GF-1遥感数据在土地利用/覆盖分类中的实际应用潜力,且有效提高了各种土地利用/覆盖地物类别的分类精度.
Based on the NDVI time-series data,NDWI data,MNDWI data and some other index data which were obtained from the GF-1 multi-temporal data as well as the Landsat8 OLI images and DEM data,the rules of extracting the spatial,multi-temporal and shape features of land objects were derived.A land use/cover classification method of complex terrains based on the GF-1 data was constructed according to those rules and the multi-layer information extraction method.With Guangzhou as the test area,the methods as well as the maximum likelihood,the minimum distance method and the land use/cover classification method of complex terrains based on the GF-1 data were used to class the land used/cover.The results showed that the overall accuracy of classifying the use/cover of land of complex terrains based on the GF-1 data is 85.86%.Besides,the accuracy of extraction of some land objects goes higher than 95%.Compared with the maximum likelihood method and the minimum distance method,this method increases the accuracy by 4.62%and 12.24%respectively,which shows that it can improve the application of GF-1 data in land use/cover classification and increase its accuracy.
作者
欧健滨
罗文斐
刘畅
OU Jianbin;LUO Wenfei;LIU Chang(School of Geography,South China Normal University,Guangzhou 510631,China;Center for Sustainable Development of Villages and Towns in Guangdong-Hong Kong-Marco Greater Bay Area,South China Normal University,Guangzhou 510631,China)
出处
《华南师范大学学报（自然科学版）》
CAS
北大核心
2019年第5期92-97,共6页
Journal of South China Normal University(Natural Science Edition)
基金
国家科技重大专项——高分辨率对地观测系统重大专项(11-Y20A40-9002-15/17)
