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  • Semi-supervised hyperspectral band selection via sparse linear regression and hypergraph models

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    Author(s)
    Guo, Zhouxiao
    Yang, Haichuan
    Bai, Xiao
    Zhang, Zhihong
    Zhou, Jun
    Griffith University Author(s)
    Zhou, Jun
    Year published
    2013
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    Abstract
    Band selection is an important step towards effective and efficient object classification in hyperspectral imagery. In this paper, we propose a semi-supervised learning method for band selection based on a sparse linear regression model. This model uses a least absolute shrinkage and selection operator to compute the regression coefficients from both labeled and unlabeled samples. These coefficients are then used to compute a contribution score for each band, which allows bands with high scores being selected for the testing step. During this process, unlabeled samples also contribute to the coefficients calculation. In order ...
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    Band selection is an important step towards effective and efficient object classification in hyperspectral imagery. In this paper, we propose a semi-supervised learning method for band selection based on a sparse linear regression model. This model uses a least absolute shrinkage and selection operator to compute the regression coefficients from both labeled and unlabeled samples. These coefficients are then used to compute a contribution score for each band, which allows bands with high scores being selected for the testing step. During this process, unlabeled samples also contribute to the coefficients calculation. In order to propagate the labels to these samples, a hypergraph is first built to describe the relationship between labeled and unlabeled samples. This leads to an adjacency matrix whose entries are the sum of corresponding weights of hyperedges. Then matrix subspace learning method is used to estimate the labels of unlabeled samples. The proposed method is evaluated on the APHI dataset. Comparison with several baseline methods has shown the advantages of the proposed method on the pixel-level classification.
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    Conference Title
    2013 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS)
    Publisher URI
    http://www.igarss2013.org/
    Copyright Statement
    © 2013 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.
    Subject
    Image processing
    Publication URI
    http://hdl.handle.net/10072/58773
    Collection
    • Conference outputs

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