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浙江大学学报(工学版)
J4  2013, Vol. 47 Issue (8): 1403-1410    DOI: 10.3785/j.issn.1008-973X.2013.08.012
电气工程     
组合核支持向量机高光谱图像分类
厉小润1, 朱洁尔1, 王晶1, 赵辽英2
1. 浙江大学 电气工程学院,浙江 杭州 310027；2. 杭州电子科技大学 计算机应用技术研究所, 浙江 杭州 310018
Hyperspectral image classification based on compsite kernels support vector machine
LI Xiao-run1, ZHU Jie-er1, WANG Jing1, ZHAO Liao-ying2
1 College of Electrical Engineering,Zhejiang University,Hangzhou 310027, China； 2. Institute of Computer Application Technology, HangZhou Dianzi University,Hangzhou 310018, China
 全文: PDF 
摘要：

为了提高高光谱遥感图像分类中空间信息的利用率,提出一种将空间邻域信息和光谱信息结合的组合核支持向量机(SVM)学习算法.用SVM进行预分类,从分类结果图提取各像素的空间邻域特征,与光谱特征结合构造组合核SVM进行分类,并再次提取空间邻域特征进行多次空-谱信息组合核SVM迭代分类,如此迭代10次,从中选择合适的结果作为最终输出.结果表明,该方法对传统支持向量机的分类精度提升幅度可达10%左右.同时,与其他组合核支持向量机相比,该算法用更少的训练样本获得了更高分类精度.
关键词：  ;  高光谱图像分类;  支持向量机;  空间邻域;  组合核    
Abstract:

To improve the utilization of spatial information when classifying hyperspectral images, this paper proposes a composite kernel SVM algorithm combining spatial and spectral information. First, the hyperspectral image was classified into a map using conventional SVM. The spatial-contextual features were then extracted based on the classified map, and combined with spectral information to construct a composite kernel SVM for classification. The spatial-contextual features were extracted again and the composite kernel SVM classified the image iteratively. The process was repeated 10 times and a proper one was chosen as the last outcome. The results show that the method increases the overall accuracy by around 10%, compared with conventional SVM. In addition, the method also demands much less training samples than usual SVM.
Key words: hyperspectral image classification    support vector machine    spatial-contextual    composite kernel
出版日期: 2013-09-05
:  TP 751.1  
基金资助:

国家自然科学基金资助项目（61171152）；教育部支撑计划项目资助项目（625010216）；浙江省自然科学基金资助项目（LY13F020044）.
作者简介: 厉小润（1970—）,男,研究员,主要从事模式识别、遥感图像分析、计算机应用方面研究.
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引用本文:

厉小润, 朱洁尔, 王晶, 赵辽英. 组合核支持向量机高光谱图像分类[J]. J4, 2013, 47(8): 1403-1410.

LI Xiao-run, ZHU Jie-er, WANG Jing, ZHAO Liao-ying. Hyperspectral image classification based on compsite kernels support vector machine. J4, 2013, 47(8): 1403-1410.

链接本文:

http://www.zjujournals.com/xueshu/eng/CN/10.3785/j.issn.1008-973X.2013.08.012    或    http://www.zjujournals.com/xueshu/eng/CN/Y2013/V47/I8/1403

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