| 引用本文: | 张宇航,张晔.SVM和RVM对高光谱图像分类的应用潜能分析[J].哈尔滨工业大学学报,2012,44(3):34.DOI:10.11918/j.issn.0367-6234.2012.03.007 |
| ZHANG Yu-hang,ZHANG Ye.Potential analysis between SVM and RVM for hyperspectral imagery classification[J].Journal of Harbin Institute of Technology,2012,44(3):34.DOI:10.11918/j.issn.0367-6234.2012.03.007 |
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| 摘要: |
| 针对高光谱图像分类一直面临的小样本、非线性及高维数等问题,分别从原理和实验两个方面分析比较了两种最新的核学习方法——支持向量机(SVM)和相关向量机(RVM)在高光谱图像分类中的异同点．通过对稀疏性、运算时间及分类精度的实验仿真,结果表明:与SVM相比,RVM 模型更加稀疏,从而测试时间更短,更有利于大数据量在线测试;然而,RVM的缺点是分类精度略低于SVM．基于此,本文利用Fisher线性鉴别分析(FLDA)技术,在分类前对高光谱数据作可分性预处理,一方面可以降低数据维数、减少计算量,另一方面可以有效地提高小样本区域的分类精度,进而提高RVM的总体分类精度,使得RVM与SVM相比在高光谱图像精细分类方面更具优势． |
| 关键词: 高光谱图像 分类 支持向量机 相关向量机 |
| DOI：10.11918/j.issn.0367-6234.2012.03.007 |
| 分类号:TB303 |
| 基金项目:国家自然科学基金资助项目（60972143）; 博士点基金资助项目（20092302110033）. |
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| Potential analysis between SVM and RVM for hyperspectral imagery classification |
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ZHANG Yu-hang,ZHANG Ye
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| Abstract: |
| To deal with the problems of limited samples, high dimension and non-linear in hyperspectral imagery classification, two new techniques, support vector machine (SVM) and relevance vector machine (RVM), are researched in this paper. Similarities and differences are compared and analyzed between SVM and RVM in hyperspectral imagery classification theoretically and experimentally. By simulations and experiments on classification accuracy, computational cost and sparsity, the results show that RVM model is sparser compared with SVM, which makes its test time much shorter, and thus more suitable for online testing of large amount of hyperspectral data. However, the main drawback of RVM is that its classification accuracy is slightly lower than that of SVM. To improve this performance, Fisher linear discriminant analysis (FLDA) is utilized before classification as a pre-processing to make transformation of hyperspectral data. In this way, not only the dimension of image is reduced, but also the classification accuracy of RVM is improved, especially in small land-cover patches, which makes the application of RVM more widely. |
| Key words: hyperspectral imagery classification support vector machine (SVM) relevance vector machine (RVM) |
