Optimizing Subspace SVM Ensemble for Hyperspectral Imagery Classification
Optimizing Subspace SVM Ensemble for Hyperspectral Imagery Classification
复制标题
优化子空间 SVM 集成以进行高光谱图像分类
DOI：
10.1109/jstars.2014.2307356
复制
发表时间：
2014-04-01
影响因子：
5.5
通讯作者：
Lin, Zhouhan
中科院分区：
文献类型：
--
作者：
Chen, Yushi;Zhao, Xing;Lin, Zhouhan
在高光谱遥感图像分类中，支持向量机（SVM）的集成系统，如随机子空间SVM集成（RSSE），在鲁棒性和整体精度上明显优于单一SVM。在本文中，我们引入了一种新的子空间机制，优化子空间SVM Ensemble（OSSE），以提高RSSE通过选择个别SVM的歧视子空间。该框架是基于遗传算法（GA），采用Jeffries-Matusita（JM）距离作为标准，优化选定的子空间。优化子空间的组合比随机子空间更适合于分类，同时具有在系综内容纳所需多样性的能力。这些修改提高了单个分类器的准确性;因此，存在更好的整体准确性。两个高光谱数据集的分类实验表明，我们提出的OSSE获得良好的性能相比，RSSE，单一的SVM，和其他集成GA优化SVM。
In hyperspectral remote sensing image classification, ensemble systems with support vector machine (SVM), such as the Random Subspace SVM Ensemble (RSSE), have significantly outperformed single SVM on the robustness and overall accuracy. In this paper, we introduce a novel subspace mechanism, the Optimizing Subspace SVM Ensemble (OSSE), to improve RSSE by selecting discriminating subspaces for individual SVMs. The framework is based on Genetic Algorithm (GA), adopting the Jeffries-Matusita (JM) distance as a criterion, to optimize the selected subspaces. The combination of optimizing subspaces is more suitable for classification than the random one, at the same time having the ability to accommodate requisite diversity within the ensemble. The modifications have improved the accuracies of individual classifiers; as a result, better overall accuracies are present. Experiments on the classification of two hyperspectral datasets reveal that our proposed OSSE obtains sound performances compared with RSSE, single SVM, and other ensemble with GA to optimize SVM.