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  • 3-D Gaussian-Gabor Feature Extraction and Selection for Hyperspectral Imagery Classification

    Author(s)
    Jia, Sen
    Zhuang, Jiayue
    Deng, Lin
    Zhu, Jiasong
    Xu, Meng
    Zhou, Jun
    Jia, Xiuping
    Griffith University Author(s)
    Zhou, Jun
    Year published
    2019
    Metadata
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    Abstract
    Hyperspectral remote sensing imagery provides valuable and rich information to distinguish the characteristics of materials. However, this advantage of hyperspectral imagery often encounters the problem of a limited amount of training samples, which is caused by the difficulty of manually labeling. Fortunately, the spatial distribution of surface objects can be integrated with the spectral signature to improve the discriminative ability. In this paper, a 3-D Gaussian-Gabor feature extraction and selection framework has been proposed for hyperspectral image classification. First, a bank of 3-D Gaussian-Gabor filters are ...
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    Hyperspectral remote sensing imagery provides valuable and rich information to distinguish the characteristics of materials. However, this advantage of hyperspectral imagery often encounters the problem of a limited amount of training samples, which is caused by the difficulty of manually labeling. Fortunately, the spatial distribution of surface objects can be integrated with the spectral signature to improve the discriminative ability. In this paper, a 3-D Gaussian-Gabor feature extraction and selection framework has been proposed for hyperspectral image classification. First, a bank of 3-D Gaussian-Gabor filters are convolved with the concatenated data of both extended multi-attribute profile (EMAP) features and raw hyperspectral data. Second, an improved fast density peak clustering (IFDPC) method is introduced to select the most representative features from each extracted 3-D Gaussian-Gabor feature cube. Finally, the retained features are combined together to accomplish the classification task. The proposed method is thus named as GG-IFDPC. Three real hyperspectral imagery data sets have been utilized, and the experiments demonstrate the advantages of the proposed GG-IFDPC approach over the compared ones.
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    Journal Title
    IEEE Transactions on Geoscience and Remote Sensing
    DOI
    https://doi.org/10.1109/tgrs.2019.2923213
    Note
    This publication has been entered into Griffith Research Online as an Advanced Online Version.
    Subject
    Geophysics
    Geomatic engineering
    Publication URI
    http://hdl.handle.net/10072/386728
    Collection
    • Journal articles

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