Classification in High-Dimensional Feature Spaces-Assessment Using SVM, IVM and RVM With Focus on Simulated EnMAP Data
Classification in High-Dimensional Feature Spaces-Assessment Using SVM, IVM and RVM With Focus on Simulated EnMAP Data
复制标题
DOI：
10.1109/jstars.2012.2190266
复制
发表时间：
2012-04-01
影响因子：
5.5
通讯作者：
Hinz, Stefan
中科院分区：
文献类型：
--
作者：
Braun, Andreas Ch;Weidner, Uwe;Hinz, Stefan
支持向量机（SVM）在整个遥感界越来越多地用于方法论和面向应用的研究。它们的主要优点是分类的准确性，以及它们几乎可以应用于任何类型的遥感数据集。特别是研究高光谱或其他高维数据集的研究人员倾向于支持向量机，因为与为多光谱数据集设计的分类器相比，支持向量机受休斯现象的影响要小得多。由于这些问题，许多研究人员发表了大量对SVM的增强。许多这些增强的目的是引入概率分布和贝叶斯定理。在本文中，我们评估和比较了支持向量机和两种增强方法——导入向量机（IVM）和相关向量机（RVM）在环境制图与分析程序EnMAP模拟数据集上的分类结果。
Support Vector Machines (SVM) are increasingly used in methodological as well as application oriented research throughout the remote sensing community. Their classification accuracy and the fact that they can be applied on virtually any kind of remote sensing data set are their key advantages. Especially researchers working with hyperspectral or other high dimensional datasets tend to favor SVMs as they suffer far less from the Hughes phenomenon than classifiers designed for multispectral datasets do. Due to these issues, numerous researchers have published a broad range of enhancements on SVM. Many of these enhancements aim at introducing probability distributions and the Bayes theorem. Within this paper, we present an assessment and comparison of classification results of the SVM and two enhancements-Import Vector Machines (IVM) and Relevance Vector Machines (RVM)-on simulated datasets of the Environmental Mapping and Analysis Program EnMAP.