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Discriminative weighted band selection via one-class SVM for hyperspectral imagery

机译:通过一类SVM对高光谱图像进行判别加权频带选择

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摘要

Abstract:In the task of hyperspectral image classification, band selection is often adopted to select a subset of informative bands to reduce the computation and storage cost. We propose a supervised band selection method which allows calculation of a discriminative weight for each band. Specifically, we consider discriminative bands as those that contribute more positive scores to a one-class classifier than those for other classes during the training stage. Based on this observation, we learn discriminative a band weight vector for each class, then bands with larger discriminative weights can be selected. Our method can be efficiently solved in one-class SVM framework. Experimental results demonstrate the effectiveness of our method.
机译:摘要:在高光谱图像分类任务中,通常采用波段选择来选择信息波段的子集,以减少计算和存储成本。我们提出了一种监督频段选择方法,该方法可以计算每个频段的判别权重。具体而言,我们将判别性条带视为在训练阶段对一类分类器的贡献高于其他类的正条带。基于此观察,我们为每个类别学习了判别权重向量,然后可以选择判别权重较大的频带。我们的方法可以在一类SVM框架中有效解决。实验结果证明了我们方法的有效性。

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