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    Unsupervised iterative CEM-clustering based multiple Gaussian feature extraction for hyperspectral image classification

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    109861M.pdf (1.066Mb)
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    https://www.spiedigitallibrary.org/conference-proceedings-of-spie/10986/109861M/Unsupervised-iterative-CEM-clustering-based-multiple-Gaussian-feature-extraction-for/10.1117/12.2519160.full?SSO=1
    Permanent Link
    https://doi.org/10.1117/12.2519160
    http://hdl.handle.net/11603/14979
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    • UMBC Computer Science and Electrical Engineering Department
    • UMBC Faculty Collection
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    Author/Creator
    Xue, Bai
    Zhong, Shengwei
    Shang, Xiaodi
    Hu, Peter F.
    Chang, Chein-I
    Date
    2019-05-14
    Type of Work
    10 pages
    Text
    conference papers and proceedings
    Citation of Original Publication
    Bai Xue, Shengwei Zhong, Xiaodi Shang, Peter F. Hu, and Chein-I Chang "Unsupervised iterative CEM-clustering based multiple Gaussian feature extraction for hyperspectral image classification", Proc. SPIE 10986, Algorithms, Technologies, and Applications for Multispectral and Hyperspectral Imagery XXV, 109861M (14 May 2019); https://doi.org/10.1117/12.2519160
    Rights
    This item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author.
    © 2019 Society of Photo-Optical Instrumentation Engineers (SPIE). One print or electronic copy may be made for personal use only. Systematic reproduction and distribution, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper are prohibited.
    Subjects
    unsupervised method
    hyperspectral image classification
    feature extraction
    multiple Gaussian features
    Abstract
    Recently, many spectral-spatial hyperspectral image classification techniques have been developed, such as widely used EPF-based and composite kernel-based approaches. However, the performance of these types of spectral-spatial approaches are generally depends on both techniques and its guided spatial feature information. To address this issue, an unsupervised subpixel detection based hyperspectral feature extraction for classification approach is proposed in this paper. Harsany-Farrand-Chang (HFC) method is utilized to estimate the number of distinct features of hyperspectral image can be decomposed into, and simplex growing algorithm (SGA) is utilized to generate endmembers as initial condition for K-means clustering. Subpixel detection maps are generated by constrained energy minimization (CEM) using centroid of K-means clusters. To capture spatial information, multiple Gaussian feature maps are generated by applying Gaussian spatial filters with different σ on CEM detection maps, and PCA is used to reduce the dimension of multiple Gaussian feature maps, and feedback it into hyperspectral band images to reprocess K-means in an iteration manner. The proposed unsupervised approach is evaluated by supervised approaches such as iterative CEM (ICEM), EPF-based, and composite kernel-based methods, and results shows that most classification performance is improved.


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    Albin O. Kuhn Library & Gallery
    University of Maryland, Baltimore County
    1000 Hilltop Circle
    Baltimore, MD 21250
    www.umbc.edu/scholarworks

    Contact information:
    Email: scholarworks-group@umbc.edu
    Phone: 410-455-3544


    If you wish to submit a copyright complaint or withdrawal request, please email mdsoar-help@umd.edu.