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国土资源遥感  2013, Vol. 25 Issue (4): 174-179    DOI: 10.6046/gtzyyg.2013.04.28
  技术应用 本期目录 | 过刊浏览 | 高级检索 |
CART集成学习方法估算平原河网区不透水面覆盖度
李晓宁1, 张友静1,2, 佘远见1, 陈立文1, 陈静欣3
1. 河海大学地球科学与工程学院, 南京 210098;
2. 河海大学水文水资源与水利工程科学国家重点实验室, 南京 210098;
3. 江苏省测绘产品质量监督检验站, 南京 210013
Estimation of impervious surface percentage of river network regions using an ensemble leaning of CART analysis
LI Xiaoning1, ZHANG Youjing1,2, SHE Yuanjian1, CHEN Liwen1, CHEN Jingxin3
1. School of Earth Sciences and Engineering, Hohai University, Nanjing 210098, China;
2. State Key Laboratory of Hydrology-Water Resource and Hydraulic Engineering, Hohai University, Nanjing 210098, China;
3. Surveying Products Quality Supervision Station of Jiangsu Province, Nanjing 210013, China
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摘要 快速扩展的不透水面已成为影响高密度河网生态系统的主要因素。以平原河网城市的典型区域苏锡常地区为研究区,提出了一种基于分类与回归树(classification and regression tree,CART)集成学习的不透水面覆盖度(impervious surface percentage,ISP)遥感估算方法,利用Landsat TM数据构建多源特征集,采用变精度粗糙集进行数据约简,以获取CART决策树的最佳属性变量,结果优于传统的单一CART方法,但得到的初始估算结果中ISP高值区低估现象较为严重,借助温度植被干旱指数(temperature vegetation dryness index,TVDI)与ISP的相关性,寻找后处理规则对其进行改善。实验结果表明,经变精度粗糙集进行属性约简和TVDI后处理的CART集成学习方法估算精度明显提高,ISP估算值与ISP参考值之间的均方根误差为10.0%,决定系数为0.89,可用于平原河网地区ISP的估算。
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唐敏
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刘波
关键词 无人机影像局部增强匹配可变窗口梯度差    
Abstract:The rapid expansion of impervious surface has become a major factor affecting ecosystem health of the high density river network. This paper provides an approach to estimate impervious surface percent (ISP) through the ensemble leaning of CART analysis based on variable precision rough sets (VPRS). Landsat TM and ALOS imagery were utilized to construct the ISP predictive model; then, in order to get the best attribute variables of CART decision tree, the authors adopted VPRS to extract optimum feature subset from multi-source feature sets. The results illustrate the validity of this ensemble leaning, and prove that this method can obtain estimated accuracy better than the traditional single CART method. However, in the initial estimation results, ISP's high value area is underestimated relatively seriously. The authors have discovered that the temperature vegetation dryness index (TVDI) and ISP have an intensive relationship with each other: the increase of ISP will cause the increase of local TVDI significantly. Therefore, the post-processing rule extracted from the relationship is used to improve the results. According to the verification results, the method combined with VPRS reduction and post-processing rule in CART algorithm has fairly higher analysis precision than the traditional single CART learning algorithm. The root mean square error between estimated ISP value and reference ISP is 10.0%, with the correlation coefficient being 0.89, so it can be used to estimate the ISP in plain river network region.
Key wordsUAV images    local enhancement    match    variable window    gradient difference
收稿日期: 2013-01-05      出版日期: 2013-10-21
:  TP79  
基金资助:国家重点基础研究发展计划(973计划)(编号: 2010CB951101)和水利部公益性行业科研专项项目(编号: 201101024)共同资助。
作者简介: 李晓宁(1988- ),女,硕士研究生,主要从事地理信息系统与遥感研究。E-mail: lixiaoning158@163.com。
引用本文:   
李晓宁, 张友静, 佘远见, 陈立文, 陈静欣. CART集成学习方法估算平原河网区不透水面覆盖度[J]. 国土资源遥感, 2013, 25(4): 174-179.
LI Xiaoning, ZHANG Youjing, SHE Yuanjian, CHEN Liwen, CHEN Jingxin. Estimation of impervious surface percentage of river network regions using an ensemble leaning of CART analysis. REMOTE SENSING FOR LAND & RESOURCES, 2013, 25(4): 174-179.
链接本文:  
https://www.gtzyyg.com/CN/10.6046/gtzyyg.2013.04.28      或      https://www.gtzyyg.com/CN/Y2013/V25/I4/174
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