| VHSR图像基于分割对象分类器性能评价 |
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| 引用本文： | 于海洋,甘甫平,武法东,党福星. VHSR图像基于分割对象分类器性能评价[J]. 国土资源遥感, 2008, 0(2): 30-35 |
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| 作者姓名： | 于海洋 甘甫平 武法东 党福星 |
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| 作者单位： | 1. 中国地质大学(北京)地球科学与资源学院,北京,100083
2. 中国国土资源航空物探遥感中心,北京,100083 |
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| 基金项目： | 中国地调局地质调查计划项目 |
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| 摘 要： | 对比研究了平行六面体、最近邻分类法、最大似然法、神经网络等经典分类算法以及近年来新发展的支持向量机分类算法在基于分割对象的高分辨率遥感图像分类中的性能,详细分析了不同内积核函数对于支持向量机分类的影响。对两个试验区进行试验的结果表明,支持向量机分类算法分类精度得到明显改善,同时分类结果受参数、样本选择等影响较小,稳定性好。
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| 关 键 词： | 基于对象图像分析 分类 IKONOS QuickBird 高分辨率 SVM |
| 文章编号： | 1001-070X(2008)02-0030-05 |
| 修稿时间： | 2007-10-23 |
THE PERFORMANCE OF OBJECT-BASED CLASSIFIERS IN THE CLASSIFICATION OF VHSR IMAGE |
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| YU Hai-yang,GAN Fu-ping,WU Fa-dong,DANG Fu-xing. THE PERFORMANCE OF OBJECT-BASED CLASSIFIERS IN THE CLASSIFICATION OF VHSR IMAGE[J]. Remote Sensing for Land & Resources, 2008, 0(2): 30-35 |
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| Authors: | YU Hai-yang GAN Fu-ping WU Fa-dong DANG Fu-xing |
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| Abstract: | The parallelepiped classifier(PC),minimum distance classifier(MDC),Maximum Likelihood Classifier(MLC),Neural network(NN) and,especially,the newly developed Support Vector Machines(SVM) were assessed in the object-based image analysis of VHSR data.The impacts of kernel configuration on the performance of the SVM and of the selection of training data of the four classifiers were also evaluated.The result reveals that SVM can improve the accuracy significantly,and is by far more stable than other algorithms in the classification of VHSR data based on OBIA. |
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| Keywords: | Object-based image analysis Classification IKONOS QuickBird High resolution SVM |
| 本文献已被 CNKI 维普 万方数据 等数据库收录！ |
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