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

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Yan, Yijun and Ren, Jinchang and Liu, Qiaoyuan and Zhao, Huimin and Sun, Haijiang and Zabalza, Jaime (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, 20. pp. 1-5. ISSN 1545-598X (https://doi.org/10.1109/LGRS.2021.3121565)

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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.

ORCID iDs

Yan, Yijun, Ren, Jinchang, Liu, Qiaoyuan, Zhao, Huimin, Sun, Haijiang and Zabalza, Jaime ORCID logoORCID: https://orcid.org/0000-0002-0634-1725;
  • Item type:Article
    ID code:79346
    Dates:
    Date
    Event
    19 October 2021
    Published
    19 October 2021
    Published Online
    14 October 2021
    Accepted
    Notes:© 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:27 Jan 2022 15:49
    Last modified:11 Nov 2024 13:21
    Related URLs:
    URI:https://strathprints.strath.ac.uk/id/eprint/79346
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