论文标题
过滤器和包装器方法的杂交降低和分类高光谱图像
Hybridization of filter and wrapper approaches for the dimensionality reduction and classification of hyperspectral images
论文作者
论文摘要
高光谱图像的高维度通常会为图像处理带来沉重的计算负担。因此，降低尺寸通常是为了消除无关，嘈杂和冗余带的必要步骤。因此，提高分类精度。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频段，以提高计算速度和预测准确性。因此，我们已经通过选择带选择了高光谱图像的尺寸，提出了一种混合算法。所提出的方法结合了相互信息增益（MIG），最小冗余最大相关性（MRMR）和FANO的误差概率与支持向量机频带消除（SVM-PF）。将所提出的方法与基于相互信息的有效再现过滤器方法进行了比较。 HSI AVIRIS 92AV3C的实验结果表明，所提出的方法的表现优于再现过滤器。 关键字 - 高光谱图像，分类，频带选择，过滤器，包装器，互信息，信息增益。
The high dimensionality of hyperspectral images often imposes a heavy computational burden for image processing. Therefore, dimensionality reduction is often an essential step in order to remove the irrelevant, noisy and redundant bands. And consequently, increase 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 bands, for improving computational speed and prediction accuracy. Hence, we have proposed a hybrid algorithm through band selection for dimensionality reduction of hyperspectral images. The proposed approach combines mutual information gain (MIG), Minimum Redundancy Maximum Relevance (mRMR) and Error probability of Fano with Support Vector Machine Bands Elimination (SVM-PF). The proposed approach is compared to an effective reproduced filters approach based on mutual information. Experimental results on HSI AVIRIS 92AV3C have shown that the proposed approach outperforms the reproduced filters. Keywords - Hyperspectral images, Classification, band Selection, filter, wrapper, mutual information, information gain.
