Classification of Hyperspectral Images With Regularized Linear Discriminant Analysis
Classification of Hyperspectral Images With Regularized Linear Discriminant Analysis
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DOI：
10.1109/tgrs.2008.2005729
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发表时间：
2009-03-01
影响因子：
8.2
通讯作者：
Camps-Valls, Gustavo
中科院分区：
文献类型：
--
作者：
Bandos, Tatyana V.;Bruzzone, Lorenzo;Camps-Valls, Gustavo
针对高光谱遥感图像中训练样本数量与光谱特征数量比例较小的情况，采用线性判别分析（LDA）方法对高光谱遥感图像进行分类。在这些特殊的病态问题中，一个可靠的LDA需要引入正则化来解决问题。尽管如此，在这样一个具有挑战性的场景中，得到的正则化LDA （RLDA）对正则化参数的调优非常敏感。在此背景下，我们在遥感界介绍了Ye等人最近提出的RLDA的有效版本，以应对关键的病态问题。此外，将几种基于LDA的分类器（即惩罚LDA、正交LDA和不相关LDA）与标准LDA和RLDA进行了理论和实验比较。通过简单的例子强调了方法差异，并对与高光谱遥感图像分类有关的几个不适定问题进行了详尽的测试。实验结果证实了本文提出的RLDA技术的有效性，并指出了其他已分析的LDA技术在关键病态高光谱图像分类问题中的主要特性。
This paper analyzes the classification of hyperspectral remote sensing images with linear discriminant analysis (LDA) in the presence of a small ratio between the number of training samples and the number of spectral features. In these particular ill-posed problems, a reliable LDA requires one to introduce regularization for problem solving. Nonetheless, in such a challenging scenario, the resulting regularized LDA (RLDA) is highly sensitive to the tuning of the regularization parameter. In this context, we introduce in the remote sensing community an efficient version of the RLDA recently presented by Ye et al. to cope with critical ill-posed problems. In addition, several LDA-based classifiers (i.e., penalized LDA, orthogonal LDA, and uncorrelated LDA) are compared theoretically and experimentally with the standard LDA and the RLDA. Method differences are highlighted through toy examples and are exhaustively tested on several ill-posed problems related to the classification of hyperspectral remote sensing images. Experimental results confirm the effectiveness of the presented RLDA technique and point out the main properties of other analyzed LDA techniques in critical ill-posed hyperspectral image classification problems.