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遥感技术与应用  2012, Vol. 27 Issue (5): 706-711    DOI: 10.11873/j.issn.1004-0323.2012.5.706
图像与数据处理     
基于PCA和多尺度纹理特征提取的高分辨率遥感影像分类
刘友山1,2,吕成文1,2,祝凤霞1,2,高 超1,2
(1.安徽师范大学国土资源与旅游学院,安徽 芜湖 241003;2.资源环境与地理信息工程安徽省工程技术研究中心,安徽 芜湖 241003)
Extraction of High Spatial Resolution Remote Sensing Image Classification based on PCA and Multi-scale Texture Feature
Liu Youshan1,2,Lv Chengwen1,2,Zhu Fengxia1,2,Gao Chao1,2
(1.College of Territorial Resources and Tourism,Anhui Normal University,Wuhu 241003,China;
2.Anhui Engineering Technology Research Center of Resources Environment and GIS,Wuhu 241003,China)
 全文: PDF(3182 KB)  
摘要:

城市地物类型多样,空间分布复杂,而且地物具有多尺度性,不同的地物类型具有不同的纹理表达尺度。利用主成分分析法(PCA)对高分辨率遥感影像进行处理,以减少数据量、抑制噪声、突出主要信息。在此基础上,利用灰度共生矩阵法对PCA的第一主成分进行纹理特征提取,选择最佳的多尺度纹理组合进行决策树分类。实验结果表明:基于PCA和多尺度纹理特征的决策树分类方法能够有效地提取地物信息,分类精度达到82.4%,Kappa系数为0.78。

关键词: 主成分分析多尺度纹理特征高分辨率    
Abstract:

The types of urban ground objects and their spatial distribution are complex.And the ground objects are multi-scale,different types of urban ground objects have different texture scale.The paper uses Principal Component Analysis(PCA) to deal with high\|resolution remote sensing images in order to reduce the quantity of data,suppress the noise,and highlight important information.On this basis,this paper extracts the texture features from the first principal component of PCA on basis of Gray Level Co-occurrence Matrix,and chooses the best combination of multi-scale textures to decision tree classification.The results show that the method of the decision tree classification based on PCA and multi-scale texture can extract the types of ground objects effectively.The precision of classification is 82.4%and Kappa coefficient is 0.78.

Key words: Principal Component Analysis(PCA)    Multi-scale texture    High spatial resolution
收稿日期: 2011-10-21 出版日期: 2012-10-17
:  TP 75  
基金资助:

安徽省教育厅自然科学重点项目(KJ2010A154)。

作者简介: 刘友山(1985-),男,安徽霍邱人,硕士研究生,主要从事遥感与GIS应用研究。Email:lys09021011@sina.com。
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引用本文:

刘友山,吕成文,祝凤霞,高 超. 基于PCA和多尺度纹理特征提取的高分辨率遥感影像分类[J]. 遥感技术与应用, 2012, 27(5): 706-711.

Liu Youshan,Lv Chengwen,Zhu Fengxia,Gao Chao. Extraction of High Spatial Resolution Remote Sensing Image Classification based on PCA and Multi-scale Texture Feature. Remote Sensing Technology and Application, 2012, 27(5): 706-711.

链接本文:

http://www.rsta.ac.cn/CN/10.11873/j.issn.1004-0323.2012.5.706        http://www.rsta.ac.cn/CN/Y2012/V27/I5/706

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