摘要
采用安徽省芜湖市芜湖县南部水稻种植区2019年3月至10月的Sentinel-2时序遥感影像,利用基于像素分类的支持向量机法、最大似然法,基于3种植被归一化植被指数(NDVI),比值植被指数(RVI)和归一化差异绿度指数(NDGI)的组合分类方法提取水稻种植信息,并结合目视解译结果对各种分类方法得到的分类结果依据混淆矩阵进行精度评价。结果表明,Sentinel-2遥感影像能够快速有效提取研究区域的水稻种植信息,其中最大似然法比支持向量机法更适合提取水稻信息,并且多时相影像数据的使用和相关植被指数的采用能够明显提升水稻信息提取精度,其最佳组合的水稻总体精度高达95.5%,Kappa系数达到了0.922,可作为水稻资源调查方法的一种有效补充手段。
Sentinel-2 time series remote sensing images from March to October 2019 in the southern rice planting area of Wuhu Coun⁃ty,Wuhu City,Anhui Province were used,and the rice planting information was extracted by using the support vector machine meth⁃od,the maximum likelihood method based on pixel classification and the classification method based on the combination of 3 Normal⁃ized Difference Vegetation Index(NDVI),Ratio Vegetation Index(RVI)and Normalized Difference Greenness Index(NDGI).The re⁃sults showed that sentinel-2 remote sensing image could quickly and effectively extract the rice planting information in the study area,and the maximum likelihood method was more suitable for extracting rice information than the support vector machine method.The use of multi temporal image data and related vegetation index could significantly improve the accuracy of rice information extraction.The overall accuracy of the best combination of rice was as high as 95.5%,and the kappa coefficient was 0.922,which could be used as an effective supplementary method for rice resources investigation.
作者
汪荃
陈军军
WANG Quan;CHEN Jun-jun(Central South Exploration&Foundation Engineering Co.,Ltd.,Wuhan 430081,China;Hubei Zhengniu Geographic Information Co.,Ltd.,Huangshi 435006,Hubei,China)
出处
《湖北农业科学》
2022年第16期175-181,共7页
Hubei Agricultural Sciences
关键词
Sentinel-2
支持向量机法
最大似然法
时间序列
植被指数
Sentinel-2
support vector machine classification
maximum likelihood classification
time series
vegetation index
