论文标题
一种基于信息理论和支持向量机的新频段选择方法，用于减少高光谱图像
A new band selection approach based on information theory and support vector machine for hyperspectral images reduction and classification
论文作者
论文摘要
由几个频段组成的高光谱图像的高维度通常会对图像处理构成巨大的计算挑战。因此，光谱带的选择是去除无关，嘈杂和冗余带的重要步骤。因此提高了分类精度。但是，识别来自数百甚至数千个相关频段的有用频段是一项非平凡的任务。本文旨在确定一小部分高度歧视频段，以提高计算速度和预测准确性。因此，我们提出了一种基于联合共同信息的新策略，以衡量所选频段之间的统计依赖性和相关性，并评估每个频段对分类的相对效用。将提出的过滤方法与基于共同信息的有效再现过滤器进行了比较。使用SVM分类器对AVIRIS 92AV3C的模拟结果表明，有效提出的算法的表现优于再现的过滤器策略性能。 关键字 - 异位图像，分类，频带选择，关节相互信息，尺寸降低，相关性，SVM。
The high dimensionality of hyperspectral images consisting of several bands often imposes a big computational challenge for image processing. Therefore, spectral band selection is an essential step for removing the irrelevant, noisy and redundant bands. Consequently increasing the classification accuracy. However, identification of useful bands from hundreds or even thousands of related bands is a nontrivial task. This paper aims at identifying a small set of highly discriminative bands, for improving computational speed and prediction accuracy. Hence, we proposed a new strategy based on joint mutual information to measure the statistical dependence and correlation between the selected bands and evaluate the relative utility of each one to classification. The proposed filter approach is compared to an effective reproduced filters based on mutual information. Simulations results on the hyperpectral image HSI AVIRIS 92AV3C using the SVM classifier have shown that the effective proposed algorithm outperforms the reproduced filters strategy performance. Keywords-Hyperspectral images, Classification, band Selection, Joint Mutual Information, dimensionality reduction ,correlation, SVM.
