基于水稻特征波段的决策树分类研究

Study on Decision Tree Classification Based on Multi-temporal Characteristic Bands of Rice

doi:
10.3969/j.issn.0517-6611.2017.28.069
摘要:
以滁州市为例,结合水稻物候的特征波段,选用反映水稻物候期时相的TM数据,并基于多特征波段,构建CART决策树分类提取水稻种植面积.结果表明,植被指数、湿度因子、绿度因子、纹理特征等多特征参与CART决策树分类能够提高总体精度.基于光谱信息、植被指数和纹理特征的决策树分类的总精度比以最大似然法进行的监督分类方法提高了6.9421百分点,Kappa系数提高了0.1104.合理选用作物物候期数据及其遥感影像的特征波段能够有效降低分类误差,为地形复杂地区获取作物种植面积提新的方法.
Abstract:
Taking Chuzhou City as an example, combined with the characteristic band of rice phenology in remote sensing image, using TM data of the typical rice phenology, planting area of rice was extracted by CART decision tree classification based on the multi-feature band.The results showed that CART algorithm classification involved by vegetation index, humidity factor,green degree factor,texture feature and other characteristics can improve the overall accuracy.The decision tree classification based on spectral information, vegetation indices and texture features was 6.9421 percentage point higher in overall accuracy than that of supervised classification method based on maximum likelihood, and its Kappa coefficient increased 0.1104.So the reasonable selection of the characteristic bands of crop phenological data and remote sens-ing images could effectively reduce the classification error, which could provide new method for obtaining crop planting area extraction in the complex terrain area.
作者 张博文[1]    崔林丽[2]    史军[3]    魏培培[1]
Author:
作者单位
  1. 上海应用技术大学生态技术与工程学院,上海201418;上海市气候中心,上海200030
  2. 上海市卫星遥感与测量应用中心,上海,200030
  3. 上海市气候中心,上海,200030
刊 名: 安徽农业科学   ISTICPKU
Journal: Journal of Anhui Agricultural Sciences
年,卷(期) 2017, 45(28)
分类号 S126
关键词: 多特征选择 知识脉络    CART决策树 知识脉络    水稻 知识脉络   
Keywords: Multi-features selection    CART decision tree    Paddy rice   
机标分类号
基金项目 国家自然科学基金项目,中国清洁发展机制基金项目
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