Using class-based feature selection for the classification of hyperspectral data
Using class-based feature selection for the classification of hyperspectral data
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DOI：
10.1080/01431161.2010.486416
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发表时间：
2011-08
影响因子：
3.4
通讯作者：
Y. Maghsoudi;M. V. Valadan Zoej;M. Collins
中科院分区：
文献类型：
--
作者：
Y. Maghsoudi;M. V. Valadan Zoej;M. Collins
高光谱遥感技术的迅速发展使人们有可能收集数百个波段的遥感数据。然而，已成功地应用于多光谱数据的数据分析方法往往是有限的，在高光谱数据取得令人满意的结果。主要的问题是高维，这恶化了分类由于休斯现象。为了避免这个问题，已经提出了大量的算法，到目前为止，用于特征约简。基于多分类器的概念，我们提出了一个新的模式的特征选择过程。在这个框架中，而不是使用整个类的特征选择，我们分别采用每个类的特征选择。因此，在第一步骤中选择不同的特征子集。一旦选择了特征子集，就在这些特征子集中的每一个上训练贝叶斯分类器。最后，使用组合机制来联合收割机组合这些分类器的输出。实验进行了机载可见光/红外成像光谱仪（AVIRIS）的数据集。令人鼓舞的结果已经获得了分类精度方面，表明所提出的算法的有效性。
The rapid advances in hyperspectral sensing technology have made it possible to collect remote-sensing data in hundreds of bands. However, the data-analysis methods that have been successfully applied to multispectral data are often limited in achieving satisfactory results for hyperspectral data. The major problem is the high dimensionality, which deteriorates the classification due to the Hughes Phenomenon. In order to avoid this problem, a large number of algorithms have been proposed, so far, for feature reduction. Based on the concept of multiple classifiers, we propose a new schema for the feature selection procedure. In this framework, instead of using feature selection for whole classes, we adopt feature selection for each class separately. Thus different subsets of features are selected at the first step. Once the feature subsets are selected, a Bayesian classifier is trained on each of these feature subsets. Finally, a combination mechanism is used to combine the outputs of these classifiers. Experiments are carried out on an Airborne Visible/Infrared Imaging Spectroradiometer (AVIRIS) data set. Encouraging results have been obtained in terms of classification accuracy, suggesting the effectiveness of the proposed algorithms.