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  • Marginalized kernel-based feature fusion method for VHR object classification

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    Author(s)
    Liu, Chuntian
    Wei, Wei
    Bai, Xiao
    Zhou, Jun
    Griffith University Author(s)
    Zhou, Jun
    Year published
    2013
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    Abstract
    Many image features can be extracted from very high resolution remote sensing images for object classification. Proper feature combination is a step towards better classification performance. In this paper, we propose a logistic regressionbased feature fusion method which assigns different weights to different features. This method considers the probability that two images belongs to the same classes and the imageto- class similarity to define the similarity between two objects. This similarity is used as a marginalized kernel for the final classifier construction. Experiments on remote sensing images suggest that this ...
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    Many image features can be extracted from very high resolution remote sensing images for object classification. Proper feature combination is a step towards better classification performance. In this paper, we propose a logistic regressionbased feature fusion method which assigns different weights to different features. This method considers the probability that two images belongs to the same classes and the imageto- class similarity to define the similarity between two objects. This similarity is used as a marginalized kernel for the final classifier construction. Experiments on remote sensing images suggest that this approach is effective in various feature combination, and has outperformed the SVM baseline method.
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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
    Computer vision
    Publication URI
    http://hdl.handle.net/10072/58739
    Collection
    • Conference outputs

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