ABSTRACT

Recent research on hyperspectral image (HSI) classification has primarily focused on deep learning methods. Although these methods can automatically mine HSI classification features, they typically require many labelled samples to ensure sufficient classification performance. When there are fewer labelled training samples, the manual feature extraction design rules are critical for HSI classification. Considering the small sample problem, a slow spatial–spectral feature extraction method is proposed for HSI classification in this study. The proposed method can achieve high classification accuracy by using slow spatial–spectral features. The extracted slow feature dimension is much lower than that of the original spectral feature. Thus, the feature dimension for the HSI classification task is reduced, improving the classification efficiency. The experimental results of two real HSI datasets demonstrate that the proposed method can significantly reduce the feature dimensions and improve HSI classification accuracy.

摘要

最近对高光谱图像（HSI）分类的研究主要集中在深度学习方法上。尽管这些方法可以自动挖掘HSI分类特征，但它们通常需要许多带标签的样本才能确保足够的分类性能。当标记的训练样本较少时，手动特征提取设计规则对于HSI分类至关重要。考虑到小样本问题，本研究提出了一种慢速空间光谱特征提取方法用于HSI分类。通过使用慢速空间光谱特征，该方法可以实现较高的分类精度。提取的慢特征尺寸远低于原始光谱特征。因此，减小了用于HSI分类任务的特征维，从而提高了分类效率。