| 基于K型支持向量机的遥感图像分类新算法 |
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| 引用本文： | 王静,何建农.基于K型支持向量机的遥感图像分类新算法[J].计算机应用,2012,32(10):2832-2835. |
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| 作者姓名： | 王静 何建农 |
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| 作者单位： | 福州大学 数学与计算机科学学院,福州 350108 |
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| 基金项目： | 国家自然科学基金资助项目(50877010);福建省杰出青年科学基金资助项目(2009J06024) |
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| 摘 要： | 为了提高遥感图像的分类精度和识别速度,提出了一种基于K型支持向量机（SVM）的遥感图像分类新算法,该算法将灰度共生矩阵提取的纹理特征与光谱特征相结合进行分类。对两组Landsat ETM+数据进行分类仿真实验,结果表明,在多光谱遥感图像的分类中,新算法提高了分类效率、分类精度和泛化能力,K型SVM是一种优于径向基函数SVM的分类器。
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| 关 键 词： | K型核函数 支持向量机 纹理特征 灰度共生矩阵 遥感图像分类 |
| 收稿时间： | 2012-04-17 |
| 修稿时间： | 2012-06-04 |
New algorithm of remote sensing image classification based on K-type support vector machine |
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| WANG Jing,HE Jian-nong.New algorithm of remote sensing image classification based on K-type support vector machine[J].journal of Computer Applications,2012,32(10):2832-2835. |
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| Authors: | WANG Jing HE Jian-nong |
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| Affiliation: | College of Mathematics and Computer Science, Fuzhou University, Fuzhou Fujian 350108, China |
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| Abstract: | In order to improve the accuracy and recognition speed of the remote sensing image classification,this paper put forward a new algorithm of remote sensing image classification based on K-type Support Vector Machine(SVM),and this algorithm used texture features extracted by gray level co-occurrence matrix combined with the spectral ones for classification.The classification simulation tests were done with two groups of Landsat ETM+data.The results show that the new algorithm can improve the accuracy and efficiency of the classification,raise generalization ability,and K-type SVM is a superior classifier to the Radial Basis Function(RBF) SVM. |
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| Keywords: | K-type kernel function Support Vector Machine(SVM) texture feature Gray Level Co-occurrence Matrix(GLCM) remote sensing image classification |
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