Feature-Ensemble Based Novelty Detection for Analysing Plant Hyperspectral Datasets

A. AlSuwaidi, B. Grieve, H. Yin

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    Recently there has been a significant increase in the use of proximal or remote hyperspectral imaging systems to study plant properties, types, and conditions. Numerous financial and environmental benefits of using such systems have been
    the driving force behind this growth. This paper is concerned with analysis of hyperspectral data for detecting plant diseases and stress conditions and classifying crop types by means of advanced machine learning techniques. Main contribution of the work lies in the use of an innovative classification framework for the analysis, in which adaptive feature selection, novelty detection and ensemble learning are integrated. Three hyperspectral datasets and a non-imaging hyperspectral dataset were used in the evaluation of the proposed framework. Experimental results show significant improvements achieved by the proposed method compared to the use of empirical spectral indices and existing
    classification methods.
    Original languageEnglish
    Article number99
    Pages (from-to)1
    Number of pages15
    JournalIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
    Issue number4
    Early online date26 Jan 2018
    Publication statusPublished - 2018


    • hyperspectral imaging
    • remote sensing
    • plant monitoring
    • feature selection
    • ensemble learning
    • novelty detection
    • support vector machine


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