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.
翻译:由多个波段组成的超光谱图像的高度维度往往给图像处理带来巨大的计算挑战。 因此,光谱波段选择是消除不相关、吵闹和冗余波段的必要步骤。 从而提高分类准确性。 但是,从数百甚至数千个相关波段中确定有用的波段是一项非技术性任务。 本文旨在确定一组高度歧视的波段,以提高计算速度和预测准确性。 因此,我们提出了一项基于共同信息的新战略,以测量选定波段之间的统计依赖性和相关性,并评估每个波段与分类的相对效用。 拟议的过滤法与基于相互信息的有效复制过滤器相比较。 使用 SVM 分类器模拟高光谱图像 HSI AVIRIS 92AV3C 的结果显示,拟议的有效算法超越了再版筛选战略的性能。 关键词- Hyper光谱图像、 分类、 频段选择、 联合相互信息、 维度减少、 相互关系、 SVM 。