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  • Object classification via feature fusion based marginalized Kernels

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    104462_1.pdf (139.0Kb)
    Author(s)
    Bai, Xiao
    Liu, Chuntian
    Ren, Peng
    Zhou, Jun
    Zhao, Huijie
    Su, Yun
    Griffith University Author(s)
    Zhou, Jun
    Zhao, Huijun
    Year published
    2015
    Metadata
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    Abstract
    Various types of features can be extracted from very high resolution remote sensing images for object classification. It has been widely acknowledged that the classification performance can benefit from proper feature fusion. In this letter, we propose a softmax regression-based feature fusion method by learning distinct weights for different features. Our fusion method enables the estimation of object-to-class similarity measures and the conditional probabilities that each object belongs to different classes. Moreover, we introduce an approximate method for calculating the class-to-class similarities between different ...
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    Various types of features can be extracted from very high resolution remote sensing images for object classification. It has been widely acknowledged that the classification performance can benefit from proper feature fusion. In this letter, we propose a softmax regression-based feature fusion method by learning distinct weights for different features. Our fusion method enables the estimation of object-to-class similarity measures and the conditional probabilities that each object belongs to different classes. Moreover, we introduce an approximate method for calculating the class-to-class similarities between different classes. Finally, the obtained fusion and similarity information are integrated into a marginalized kernel to build a support vector machine classifier. The advantages of our method are validated on QuickBird imagery.
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    Journal Title
    IEEE Geoscience and Remote Sensing Letters
    Volume
    12
    Issue
    1
    DOI
    https://doi.org/10.1109/LGRS.2014.2322953
    Copyright Statement
    © 2015 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
    Geomatic engineering
    Publication URI
    http://hdl.handle.net/10072/69220
    Collection
    • Journal articles

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