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遥感技术与应用  2002, Vol. 17 Issue (1): 6-11    DOI: 10.11873/j.issn.1004-0323.2002.1.6
研究与应用     
决策树分类法及其在土地覆盖分类中的应用
李 爽1,2,丁圣彦1,钱乐祥1
(1.河南大学环境与规划学院 河南开封  475001; 2.聊城师范学院地理系 河南聊城  252059)
The Decision Tree Classification and Its
Application Research in Land Cover
LI Shuang1,2, DING Sheng-yan1, QIAN Le-xiang1
(1.College of Environment and Planning,Henan University,Kaifeng475001,China;
2.Department of Geography,Liaocheng Normal University,Liaocheng252059,China)
 全文: PDF 
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,然后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,决策树分类法有诸多优势,如:相对简单、明确、分类结构直观,另外,与以假定数据源呈一固定概率分布,然后在此基础上进行参数估计的常规分类方法相比,决策树属于严格“非参”,对于输入数据空间特征和分类标识具有更好的弹性和鲁棒性(Robust)。

关键词: 决策树分类遥感影像最大似然分类法    
Abstract:

Decision tree classification algorithms have significant potential for remote sensing data classification.In this paper, three different types decision tree classification (UDT, MDT and HDT)are presented. First, the paper discussed the algorithms structure and the algorithms theory of decision tree. Second, decision tree algorithms were used to make land cover classification from remotely sensed data, and the results were compared 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. In addition, decision tree algorithms are strictly nonparametric and, therefore, without assumptions
regarding the distribution of input data the methods are flexible and robust with respect to general classifications
among input features and class labels.

Key words: Decision tree classification    Remote sensing image    Maximum likelihood classification
收稿日期: 2001-09-26 出版日期: 2011-11-21
:  TP 75  
基金资助:

本项目研究得到河南省杰出青年科学基金资助(项目编号:0003,9920)。

作者简介: 李爽(1974-),男,讲师,主要从事GIS、RS应用研究。
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引用本文:

李 爽,丁圣彦,钱乐祥. 决策树分类法及其在土地覆盖分类中的应用[J]. 遥感技术与应用, 2002, 17(1): 6-11.

LI Shuang, DING Sheng-yan, QIAN Le-xiang. The Decision Tree Classification and Its
Application Research in Land Cover. Remote Sensing Technology and Application, 2002, 17(1): 6-11.

链接本文:

http://www.rsta.ac.cn/CN/10.11873/j.issn.1004-0323.2002.1.6        http://www.rsta.ac.cn/CN/Y2002/V17/I1/6

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