Slow feature extraction for hyperspectral image classification. Issue 5 (4th May 2021)

• Record Type: Journal Article
• Authors: Liu, Bing; Yu, Anzhu; Tan, Xiong; Wang, Ruirui
• Is Part Of: Remote sensing letters. Volume 12:Issue 5(2021)
• Page Start: 429
• Page End: 438
• Publication Date: 2021-05-04
• Subjects: Remote sensing -- Periodicals; Remote sensing; Periodicals; 621.3678
• DOI: 10.1080/2150704X.2021.1895448
• Languages: English
• ISSNs: 2150-704X

Abstract

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.