Classical supervised feature extraction methods, such as linear discriminant analysis (LDA) and nonparametric weighted feature extraction (NWFE), and search for projection directions through which the ratio of a between-class scatter matrix to a within-class scatter matrix can be maximized. The two feature extraction methods can obtain good classification results when training samples are sufficient; however, the effect is nonideal when samples are insufficient. In this study, the eigenvector spectra of LDA and NWFE are modified using spectral distribution information, which is locally unstable under the condition of a few samples. Experiments demonstrate that the proposed method outperforms several conventional feature extraction methods.

中文翻译：

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用于使用有限样本的高光谱图像分类的改进的基于特征向量的特征提取

经典的监督特征提取方法，例如线性判别分析 (LDA) 和非参数加权特征提取 (NWFE)，并搜索投影方向，通过该方向可以最大化类间散布矩阵与类内散布矩阵的比率。两种特征提取方法在训练样本充足的情况下都能获得较好的分类结果；然而，当样本不足时，效果并不理想。在本研究中，LDA 和 NWFE 的特征向量光谱使用光谱分布信息进行修改，该信息在少数样本条件下是局部不稳定的。实验表明，所提出的方法优于几种传统的特征提取方法。