An improved SVM algorithm for high spatial resolution remote sensing image classification
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摘要: 针对面向对象高分辨率遥感影像分类样本维数多、数据量大的特点,提出了一种简单的支持向量机( support vector machine,SVM)改进算法。首先对原始样本数据进行主成分分析(principal component analysis,PCA)实现降维,对降维后的样本数据进行SVM分类器训练,利用网格搜索法得出降维数据的最佳参数;以此参数作为基准,对基于原始样本数据的SVM分类器参数搜索范围进行重新设定,从而快速获取原始样本数据的最佳SVM分类器参数,并实现分类。利用2景WorldView2高分辨率影像分别对城市土地利用以及林木树种进行分类实验,比较分析传统SVM算法、仅基于PCA降维样本数据的SVM算法以及改进的SVM算法在分类精度与效率方面的差异。实验结果表明,改进的SVM算法能够快速有效地寻找最佳SVM分类器参数,并获得较高的分类精度。
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关键词:
- 高分辨率遥感影像 /
- 支持向量机(SVM) /
- 主成分分析 /
- 网格搜索法 /
- 分类性能
Abstract: Support vector machine ( SVM) algorithm has been widely used for remote sensing image classification . For high spatial resolution image classification , traditional SVM algorithm usually leads to low efficiency due to large quantities of high dimensional sample data .This paper presents a simple improved SVM algorithm with the purpose of improving both efficiency and accuracy of classification models .The algorithm first uses PCA to reduce the dimension of sample features .The grid -based method is used to search for optimal parameters for SVM classification of PCA-based samples .Then new range around the PCA -optimal parameters is set up and used for optimal parameter search based on the original sample data .Finally, SVM with the optimal parameters is used to train the original sample data and classify the image .The new algorithm was evaluated by two classification experiments based on WorldView 2 images including urban land cover land use classification and urban tree classification .Compared with the traditional SVM and SVM merely based on PCA data , the results show that the improved SVM algorithm could quickly and efficiently find the optimum parameters of the SVM classifier and achieves higher classification accuracy . -
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