Combining spectral and texture features in hyperspectral image analysis for plant monitoring
Combining spectral and texture features in hyperspectral image analysis for plant monitoring
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DOI：
10.1088/1361-6501/aad642
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发表时间：
2018-10-01
影响因子：
2.4
通讯作者：
Yin, Hujun
中科院分区：
文献类型：
--
作者：
AlSuwaidi, Ali;Grieve, Bruce;Yin, Hujun
提出了一种纹理增强的光谱分析框架，用于分类不同条件下的植物高光谱图像。区分不同的植物条件对精准农业很重要，因为它有助于检测疾病和胁迫，并优化生长控制。先进的机器学习技术被用来识别高光谱图像光谱域中的不同特征。此外，还在子带图像中探索了纹理属性。该框架在特征提取和分类决策两个阶段综合了这两个层次的属性。工作的主要关键在于利用显著的光谱和纹理特征以及一种决策融合机制来增强图像的属性，从而提高分类精度。在评估中使用了来自近端高光谱系统的两个高光谱数据集，分类精度得到了显著提高。
A texture enhanced spectral analysis framework is proposed for classifying hyperspectral images of plants of different conditions. Differentiating different plant conditions is important to precision agriculture as it helps detect diseases and stresses and optimise growth control. Advanced machine learning techniques are used to identify distinctive features in the spectral domain of hyperspectral images. In addition, texture properties are explored in the sub-band images. The framework integrates these two levels of properties at both feature extraction and classifying decision stages. The main crux of the work lies in the use of the significant spectral and texture features and a decision fusion mechanism to enhance the image properties, thus improving classification accuracy. Two hyperspectral datasets, originated from proximal hyperspectral systems, were used in the evaluation and significant improvements in classification accuracy achieved.