SpaSSA : superpixelwise adaptive SSA for unsupervised spatial-spectral feature extraction in hyperspectral image
Sun, Genyun and Fu, Hang and Ren, Jinchang and Zhang, Aizhu and Zabalza, Jaime and Jia, Xiuping and Zhao, Huimin (2022) SpaSSA : superpixelwise adaptive SSA for unsupervised spatial-spectral feature extraction in hyperspectral image. IEEE Transactions on Cybernetics, 52 (7). pp. 6158-6169. ISSN 2168-2275 (https://doi.org/10.1109/TCYB.2021.3104100)
Preview |
Text.
Filename: Sun_etal_IEEETC_2021_SpaSSA_superpixelwise_adaptive_SSA_for_unsupervised_spatial_spectral_feature_extraction.pdf
Accepted Author Manuscript License: Strathprints license 1.0 Download (1MB)| Preview |
Abstract
Singular spectral analysis (SSA) has recently been successfully applied to feature extraction in hyperspectral image (HSI), including conventional (1-D) SSA in spectral domain and 2-D SSA in spatial domain. However, there are some drawbacks, such as sensitivity to the window size, high computational complexity under a large window, and failing to extract joint spectral-spatial features. To tackle these issues, in this article, we propose superpixelwise adaptive SSA (SpaSSA), that is superpixelwise adaptive SSA for exploiting local spatial information of HSI. The extraction of local (instead of global) features, particularly in HSI, can be more effective for characterizing the objects within an image. In SpaSSA, conventional SSA and 2-D SSA are combined and adaptively applied to each superpixel derived from an oversegmented HSI. According to the size of the derived superpixels, either SSA or 2-D singular spectrum analysis (2D-SSA) is adaptively applied for feature extraction, where the embedding window in 2D-SSA is also adaptive to the size of the superpixel. Experimental results on the three datasets have shown that the proposed SpaSSA outperforms both SSA and 2D-SSA in terms of classification accuracy and computational complexity. By combining SpaSSA with the principal component analysis (SpaSSA-PCA), the accuracy of land-cover analysis can be further improved, outperforming several state-of-the-art approaches.
ORCID iDs
Sun, Genyun, Fu, Hang, Ren, Jinchang, Zhang, Aizhu, Zabalza, Jaime ORCID: https://orcid.org/0000-0002-0634-1725, Jia, Xiuping and Zhao, Huimin;-
-
Item type: Article ID code: 79437 Dates: DateEventJuly 2022Published7 August 2021AcceptedNotes: © 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting /republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. Subjects: Technology > Electrical engineering. Electronics Nuclear engineering Department: Faculty of Engineering > Electronic and Electrical Engineering Depositing user: Pure Administrator Date deposited: 03 Feb 2022 11:22 Last modified: 31 Oct 2024 01:53 Related URLs: URI: https://strathprints.strath.ac.uk/id/eprint/79437