Singular spectrum analysis method for hyperspectral imagery feature extraction: a review and evaluation.

Sun, Genyun; Fu, Hang; Zhang, Aizhu; Ren, Jinchang

Abstract

Hyperspectral remote sensing imagery (HSI) usually contains dozens to hundreds of continuous spectral bands, with the syncretism of spectrum and image, spectral continuity, which can realize fine classification of ground objects and has been widely used in agriculture, forestry, urban and marine areas. The feature extraction of HSI is the premise of hyperspectral applications and has become one of the research hotspots and frontier topics in remote sensing. In recent years, singular spectrum analysis (SSA) has been applied in HSI, achieving superior results in the extraction of spectral and spatial features, and gradually becoming an effective feature extraction method. In this paper, firstly, the research progress and existing problems of HSI feature extraction are analyzed. Secondly, the existing SSA methods are systematically summarized and reviewed. The functions, effects, advantages, and disadvantages of three types of methods, namely, spectral domain 1D-SSA, spatial domain 2D-SSA, and combined spectral-spatial domain SSA, are introduced respectively, and the classification results are verified on two publicly available HSI datasets and one China Gaofen-5 satellite HSI dataset. Finally, the SSA feature extraction is summarized and future research directions are discussed.

Citation

SUN, G., FU, H., ZHANG, A. and REN, J. 2023. Singular spectrum analysis method for hyperspectral imagery feature extraction: a review and evaluation. Cehui xuebao/Acta geodaetica et cartographica sinica [online], 15(7), pages 1148-1163. Available from: https://doi.org/10.11947/j.AGCS.2023.20220542

• Journal Article Type: Article
• Acceptance Date: Jun 17, 2023
• Online Publication Date: Jul 20, 2023
• Publication Date: Jul 31, 2023
• Deposit Date: Sep 15, 2023
• Publicly Available Date: Sep 15, 2023
• Journal: Cèhuì xuébào: acta geodaetica et cartographica Sinica
• Print ISSN: 1001-1595
• Electronic ISSN: 1001-1595
• Publisher: Surveying and Mapping Press
• Peer Reviewed: Peer Reviewed
• Volume: 52
• Issue: 7
• Pages: 1148-1163
• DOI: https://doi.org/10.11947/j.AGCS.2023.20220542
• Keywords: Hyperspectral imagery; Feature extraction; Singular spectrum analysis; Classification; Review
• Public URL: https://rgu-repository.worktribe.com/output/2079202
• Additional Information: The full text of this article is in Chinese.