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  • Spectral-texture feature extraction using statistical moments with application to object-based vegetation species classification

    Author(s)
    Li, Zhengrong
    Hayward, Ross
    Liu, Yuee
    Walker, Rodney
    Griffith University Author(s)
    Liu, Yuee
    Year published
    2011
    Metadata
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    Abstract
    The use of appropriate features to characterise an output class or object is critical for all classification problems. In order to find optimal feature descriptors for vegetation species classification in a power line corridor monitoring application, this article evaluates the capability of several spectral and texture features. A new idea of spectral-texture feature descriptor is proposed by incorporating spectral vegetation indices in statistical moment features. The proposed method is evaluated against several classic texture feature descriptors. Object-based classification method is used and a support vector machine is ...
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    The use of appropriate features to characterise an output class or object is critical for all classification problems. In order to find optimal feature descriptors for vegetation species classification in a power line corridor monitoring application, this article evaluates the capability of several spectral and texture features. A new idea of spectral-texture feature descriptor is proposed by incorporating spectral vegetation indices in statistical moment features. The proposed method is evaluated against several classic texture feature descriptors. Object-based classification method is used and a support vector machine is employed as the benchmark classifier. Individual tree crowns are first detected and segmented from aerial images and different feature vectors are extracted to represent each tree crown. The experimental results showed that the proposed spectral moment features outperform or can at least compare with the state-of-the-art texture descriptors in terms of classification accuracy. A comprehensive quantitative evaluation using receiver operating characteristic space analysis further demonstrates the strength of the proposed feature descriptors.
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    Journal Title
    International Journal of Image and Data Fusion
    Volume
    2
    Issue
    4
    DOI
    https://doi.org/10.1080/19479832.2010.546372
    Subject
    Earth Sciences not elsewhere classified
    Earth Sciences
    Information and Computing Sciences
    Publication URI
    http://hdl.handle.net/10072/44738
    Collection
    • Journal articles

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