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遥感技术与应用  2019, Vol. 34 Issue (1): 68-78    DOI: 10.11873/j.issn.1004-0323.2019.1.0068
土地利用/覆被专栏     
基于迭代CART算法分层分类的土地覆盖遥感分类
吴薇1,2,张源1,李强子1,黄慧萍1
 (1.中国科学院遥感与数字地球研究所,北京 100101;
2.中国科学院大学,北京 100049)
A Hierarchical Classification and Iterative Model based Methodfor Remote Sensing Classification of Land Cover
 Wu Wei1,2,Zhang Yuan1,Li Qiangzi1,Huang Huiping1
 (1.Institute of Remote Sensing and Digital Earth,Chinese Academy of Sciences,Beijing 100101,China;
2.University of Chinese Academy of Sciences,Beijing 100049,China)
 全文: PDF(4335 KB)  
摘要:

土地覆盖遥感分类是土地利用变化监测及其空间格局分析的重要技术手段。为了进一步提高土地覆盖遥感分类精度,提出一种基于迭代CART算法分层分类的新技术体系。首先,根据类别光谱可分性分析,确定分层分类方法的类别提取顺序依次为水体、植被、裸地和建设用地。然后,在此分类顺序下,分别确定各类别的图像分割参数和分类特征集。最后,在对象尺度上,将CART算法迭代引入分层分类过程,不断选取训练样本进行CART算法的迭代分类依次提取前3个类别,将剩余部分直接划分为建设用地。实验结果证明:该方法可以明显减轻裸地和建设用地的混分现象,获得较高精度的土地覆盖分类结果,总体精度85.76%,Kappa系数0.72。相比于SVM、CART两种单次分类方法,总体精度和Kappa系数分别提升了10.67%~16.5%和0.15~0.21。同时,该方法能够灵活调整某个类别的分类精度并具有很强的扩展性,可以为其他涉及图像分类的遥感应用领域提供方法参考。

 

关键词: 土地覆盖分层分类迭代可分性高分二号
    
Abstract: Land cover classification based on remote sensing is an important means to analyze the change and spatial pattern of land use.In order to further improve the classification accuracy,this paper proposed a hierarchical classification and iterative CART model based method for remote sensing classification of landcover.Firstly,the extraction order of land cover classes was determined based on the class separability evaluation,which was water,vegetation,bare soil and built-up land.Secondly,we selected the optimal image segmentation parameters and a set of sensitive features for each class during the hierarchical classification process.Finally,object-based training samples were selected to be fed into the iterative CART algorithm for the successive extraction of the first three classes,with the remaining unclassified objects being directly assigned to the last class.Results demonstrated that the proposed method can significantly reduce the mixture between bare soil and built-up land,and is capable of achieving landcover classification with much higher accuracy.The proposed method achieved an overall accuracy of 85.76% and a Kappa efficient of 0.72,with the performance improvements ranging from 10.67% to 16.5% and 0.15 to 0.21 as compared SVM and CART single classification methods.The classification accuracy of a specific class can be flexibly adjusted using this method,giving different purposes of classification.This method can also be easily extended to other districts and disciplines involving remote sensing image classification.
Key words: Land cover    Hierarchical classification    Iteration    Separability    GF-2
收稿日期: 2018-02-06 出版日期: 2019-04-02
ZTFLH:  TP79  
基金资助:  国土资源部公益性行业科研专项“京津冀土地优化利用一体化管控关键技术与应用”(201511010)。
作者简介: 吴薇(1991-),女,江西上饶人,博士研究生,主要从事城市遥感方面的研究。E-mail:wuwei@radi.ac.cn。
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引用本文:

吴薇, 张源, 李强子, 黄慧萍. 基于迭代CART算法分层分类的土地覆盖遥感分类[J]. 遥感技术与应用, 2019, 34(1): 68-78.

Wu Wei, Zhang Yuan, Li Qiangzi, Huang Huiping. A Hierarchical Classification and Iterative Model based Methodfor Remote Sensing Classification of Land Cover. Remote Sensing Technology and Application, 2019, 34(1): 68-78.

链接本文:

http://www.rsta.ac.cn/CN/10.11873/j.issn.1004-0323.2019.1.0068        http://www.rsta.ac.cn/CN/Y2019/V34/I1/68

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