摘要
基于决策树分类算法在遥感影像分类方面的深厚潜力 ,探讨了 3种不同的决策树算法 (UDT、MDT和HDT)。首先对决策树算法结构、算法理论进行了阐述 :具体利用决策树算法进行遥感土地覆盖分类实验 ,并把获得的结果与传统统计分类法进行比较。研究表明 ,决策树分类法相对简单、明确 ,分类结构直观 ,有诸多优势。
Decision tree classification algorithms have significant potential for remote sensing data classification. In this research, three different types decision tree classification (UDT, MDT and HDT) are present. First, the paper discussed the algorithms structure and the algorithms theory of decision tree. Second, decision tree algorithms have been used to play land cover classification from remotely sensed data, and compare the result with conventional statistics classification. The results of this research showed that decision trees have several advantages for remote sensing applications by virtue of their relatively simple, explicit, and intuitive classification structure.
出处
《地域研究与开发》
CSSCI
2003年第1期17-21,共5页
Areal Research and Development
基金
河南省杰出青年科学基金资助项目 ( 0 0 0 3
992 0 )
