Feature-Ensemble-Based Novelty Detection for Analyzing Plant Hyperspectral Datasets
Feature-Ensemble-Based Novelty Detection for Analyzing Plant Hyperspectral Datasets
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DOI：
10.1109/jstars.2017.2788426
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发表时间：
2018-04-01
影响因子：
5.5
通讯作者：
Yin, Hujun
中科院分区：
文献类型：
--
作者：
AlSuwaidi, Ali;Grieve, Bruce;Yin, Hujun
最近，已经有一个显着增加使用近端或远程高光谱成像系统来研究植物的属性，类型和条件。使用这种系统的许多财务和环境效益一直是这种增长背后的驱动力。本论文主要研究利用先进的机器学习技术对高光谱数据进行分析，以检测植物病害和胁迫条件，并对作物类型进行分类。工作的主要贡献在于使用一个创新的分类框架的分析，其中自适应特征选择，新奇检测，集成学习。三个高光谱数据集和一个非成像高光谱数据集被用于评估所提出的框架。实验结果表明，所提出的方法相比，使用经验光谱指数和现有的分类方法取得了显着的改善。
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 the 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 nonimaging 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.