Research on SVM and its application of remote sense image classification for regions of interest

doi:10.3778/j.issn.1002-8331.2009.06.070

Computer Engineering and Applications ›› 2009, Vol. 45 ›› Issue (6): 243-245.DOI: 10.3778/j.issn.1002-8331.2009.06.070

• 工程与应用 • Previous Articles Next Articles

Research on SVM and its application of remote sense image classification for regions of interest

ZENG Lian-ming^1,2,3,WU Xiang-bin¹,LIU Peng³

1.School of Geosciences and Environmental Engineering，Central South University，Changsha 410083，China
2.Information and Educational Technology Center，Foshan University，Foshan，Guangdong 528000，China
3.MilGrid Research Center，The PLA University of Science and Technology，Nanjing 210016，China

Received:2008-01-08 Revised:2008-03-31 Online:2009-02-21 Published:2009-02-21
Contact: ZENG Lian-ming

感兴趣区域遥感图像分类与支持向量机应用研究

曾联明^1,2,3,吴湘滨1,刘鹏³

1.中南大学地学与环境工程学院，长沙 410083
2.佛山科学技术学院信息中心，广东佛山 528000
3.解放军理工大学网格研究中心，南京 210016

通讯作者: 曾联明

Abstract

Abstract: A classification method and model is proposed based on SVM for remote sense image.By selecting Regions Of Interest（ROI） from the 1∶50 000 TM image of Tangshan city area，extract the feature of greenbelt，public lands，building and so on，the parameters of C and γ are achieved by cross validation method，with these textures to train and parameters to classify the RS image，the fact shows that the classification method based on SVM has a high accuracy and a fast，stably efficiency.

Key words: remote sense, image classification, Support Vector Machine（SVM）, Regions Of Interest（ROI）

摘要： 提出了基于SVM的遥感图像分类方法并构建了分类模型，该方法以唐山1∶50 000 TM局部图为分类数据来源，由用户选择感兴趣的区域，分别提取该区域绿地、公共用地和房屋的图像特征，并以此为训练样本进行训练，采取交叉校验的方法获得SVM的最优惩罚因子C和间隔γ参数进行图像分类。实验结果表明，此分类方法准确率高、稳定快捷，是SVM在遥感图像分类中的一个很好的应用。

关键词: 遥感, 图像分类, 支持向量机, 感兴趣区域

ZENG Lian-ming^1,2,3,WU Xiang-bin¹,LIU Peng³. Research on SVM and its application of remote sense image classification for regions of interest[J]. Computer Engineering and Applications, 2009, 45(6): 243-245.

曾联明^1,2,3,吴湘滨1,刘鹏³. 感兴趣区域遥感图像分类与支持向量机应用研究[J]. 计算机工程与应用, 2009, 45(6): 243-245.

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Research on SVM and its application of remote sense image classification for regions of interest

感兴趣区域遥感图像分类与支持向量机应用研究

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