Identification of spectral features in the longwave infrared (LWIR) spectra of leaves for the discrimination of tropical dry forest tree species
Identification of spectral features in the longwave infrared (LWIR) spectra of leaves for the discrimination of tropical dry forest tree species
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DOI：
10.1016/j.jag.2020.102286
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发表时间：
2021-05
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通讯作者：
Yaqian Long;B. Rivard;A. Sánchez-Azofeifa;R. Greiner;Dominica Harrison;Sen Jia
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文献类型：
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作者：
Yaqian Long;B. Rivard;A. Sánchez-Azofeifa;R. Greiner;Dominica Harrison;Sen Jia
随着长波高光谱成像系统的出现，研究揭示了这些数据用于区分树种的潜力。然而，很少有研究应用统计方法的波段选择，选择和表征功能，然后可以用于改进分类的物种水平。最近在哥斯达黎加热带干旱森林中收集了26个树种的叶光谱数据集。物种的光谱呈现整体低对比度和光谱形状的范围，其中一些物种显示光谱相似性。这促使我们的研究探索波段选择工具的性能，以帮助识别这些物种分类的关键光谱特征。与没有波段选择的结果相比，使用多种方法组合选择的波段将逻辑回归分类性能提高了3%。多种方法包括随机森林法、最小冗余最大相关法和n维光谱立体角法。集成方法选择的波段与先前根据专家知识确定的特征一致，并且可以在叶组成化合物和相关光谱特征的背景下理解。在这项研究中确定的长波高光谱波段或功能可以潜在地帮助未来的图像映射的树种在大尺度上。在植被波段分析中，建议采用集成策略，其精度和稳定性最高。
With the emergence of longwave hyperspectral imaging systems, studies are revealing the potential of these data for discriminating tree species. However, few studies have applied statistical methods of band selection to select and characterize features at the species level that can then be used for improved classification. A dataset of leaf spectra was recently collectedin-situfrom twenty-six tree species in a Costa Rican tropical dry forest. The spectra of the species present overall low contrast and a range in spectral shapes, with some species displaying spectral similarity. This motivates our study to explore the performance of band selection tools to help identify key spectral features for the classification of these species.The bands selected using an ensemble of multiple methods improved the Logistic Regression classification performance by 3% in comparison to a result without band selection. The multiple methods encompassed the random forest, minimum redundancy maximum relevance and n-dimensional spectral solid angle methods. Bands selected by the ensemble methods agree well with the features previously identified based on expert knowledge and can be understood in the context of leaf constitutional compounds and related spectral features. The longwave hyperspectral bands or features identified in this study can potentially assist the future image mapping of tree species at large scales. The ensemble strategy is recommended for the band analysis of vegetation for its highest accuracy and stability.