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  • Semi-supervised hyperspectral band selection via spectral-spatial hypergraph model

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    Author(s)
    Bai, Xiao
    Guo, Zhouxiao
    Wang, Yanyang
    Zhang, Zhihong
    Zhou, Jun
    Griffith University Author(s)
    Zhou, Jun
    Year published
    2015
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    Abstract
    Band selection is an essential step toward effective and efficient hyperspectral image classification. Traditional supervised band selection methods are often hindered by the problem of lacking enough training samples. To address this problem, we propose a semisupervised band selection method that allows contribution from both labeled and unlabeled hyperspectral pixels. This method first builds a hypergraph model from all hyperspectral samples to measure the similarity among pixels. We show that hypergraph can capture relationship among pixels in both spectral and spatial domain. In the second step, a semisupervised learning ...
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    Band selection is an essential step toward effective and efficient hyperspectral image classification. Traditional supervised band selection methods are often hindered by the problem of lacking enough training samples. To address this problem, we propose a semisupervised band selection method that allows contribution from both labeled and unlabeled hyperspectral pixels. This method first builds a hypergraph model from all hyperspectral samples to measure the similarity among pixels. We show that hypergraph can capture relationship among pixels in both spectral and spatial domain. In the second step, a semisupervised learning method is introduced to propagate class labels to unlabeled samples. Then a linear regression model with group sparsity constraint is used for band selection. Finally, hyperspectral pixels with selected bands are used to train a support vector machine (SVM) classifier. The proposed method is tested on three benchmark datasets. Experimental results demonstrate its advantages over several other band selection methods.
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    Journal Title
    IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
    Volume
    8
    Issue
    6
    DOI
    https://doi.org/10.1109/JSTARS.2015.2443047
    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
    Physical geography and environmental geoscience
    Image processing
    Geomatic engineering
    Publication URI
    http://hdl.handle.net/10072/69212
    Collection
    • Journal articles

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