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    Hyperspectral image spectral-spatial feature extraction via tensor principal component analysis

    Ren, Y. and Liao, L. and Maybank, Stephen J. and Zhang, Y. and Liu, X. (2017) Hyperspectral image spectral-spatial feature extraction via tensor principal component analysis. IEEE Geoscience and Remote Sensing Letters 14 (9), pp. 1431-1435. ISSN 1545-598X.


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    We consider the tensor-based spectral-spatial feature extraction problem for hyperspectral image classification. First, a tensor framework based on circular convolution is proposed. Based on this framework, we extend the traditional PCA to its tensorial version TPCA, which is applied to the spectral-spatial features of hyperspectral image data. The experiments show that the classification accuracy obtained using TPCA features is significantly higher than the accuracies obtained by its rivals.


    Item Type: Article
    Additional Information: (c) 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, 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 components of this work in other works.
    Keyword(s) / Subject(s): tensor model, principal component analysis, feature extraction, hyperspectral image classification
    School: School of Business, Economics & Informatics > Computer Science and Information Systems
    Depositing User: Administrator
    Date Deposited: 20 Mar 2017 11:18
    Last Modified: 04 Mar 2021 12:26


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