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PCA-domain fused singular spectral analysis for fast and noise-robust spectral-spatial feature mining in hyperspectral classification.

Yan, Yijun; Ren, Jinchang; Liu, Qiaoyuan; Zhao, Huimin; Sun, Haijiang; Zabalza, Jaime

Authors

Jinchang Ren

Qiaoyuan Liu

Huimin Zhao

Haijiang Sun

Jaime Zabalza



Abstract

The principal component analysis (PCA) and 2-D singular spectral analysis (2DSSA) are widely used for spectral domain and spatial domain feature extraction in hyperspectral images (HSI). However, PCA itself suffers from low efficacy if no spatial information is combined, whilst 2DSSA can extract the spatial information yet has a high computing complexity. As a result, we propose in this paper a PCA domain 2DSSA approach for spectral-spatial feature mining in HSI. Specifically, PCA and its variation, folded-PCA are utilized to fuse with the 2DSSA, as folded-PCA can extract both global and local spectral features. By applying 2DSSA only on a small number of PCA components, the overall computational complexity has been significantly reduced whilst preserving the discrimination ability of the features. In addition, with the effective fusion of spectral and spatial features, the proposed approach can work well on the uncorrected dataset without removing the noisy and water absorption bands, even under a small number of training samples. Experiments on two publicly available datasets have fully demonstrated the superiority of the proposed approach, in comparison to several state-of-the-art HSI classification methods and deep-learning models.

Citation

YAN, Y., REN, J., LIU, Q., ZHAO, H., SUN, H. and ZABALZA, J. 2021. PCA-domain fused singular spectral analysis for fast and noise-robust spectral-spatial feature mining in hyperspectral classification. IEEE geoscience and remote sensing letters [online], Early Access. Available from: https://doi.org/10.1109/LGRS.2021.3121565

Journal Article Type Article
Acceptance Date Oct 19, 2021
Online Publication Date Oct 19, 2021
Deposit Date Oct 21, 2021
Publicly Available Date Oct 21, 2021
Journal IEEE Geoscience and Remote Sensing Letters
Print ISSN 1545-598X
Electronic ISSN 1558-0571
Publisher IEEE Institute of Electrical and Electronics Engineers
Peer Reviewed Peer Reviewed
DOI https://doi.org/10.1109/lgrs.2021.3121565
Keywords Hyperspectral image (HSI); Spectral-spatial feature mining; Principal component analysis (PCA); Singular spectrum analysis (SSA)
Public URL https://rgu-repository.worktribe.com/output/1500504

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