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 collected in-situ from 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.

随着长波高光谱成像系统的出现，研究揭示了这些数据可用于识别树木的潜力。但是，很少有研究应用频带选择的统计方法来选择和表征物种级别的特征，然后可以将其用于改进的分类。最近在哥斯达黎加的热带干旱森林中从26种树种中就地收集了叶片光谱数据集。物种的光谱呈现总体较低的对比度，并且在光谱形状上存在一定范围，有些物种表现出光谱相似性。这激励了我们的研究，以探索谱带选择工具的性能，以帮助识别这些物种分类的关键光谱特征。

与没有选择波段的结果相比，使用多种方法选出的波段将Logistic回归分类性能提高了3％。多种方法包括随机森林，最小冗余最大相关性和n维光谱立体角方法。通过集合方法选择的谱带与先前基于专家知识确定的特征非常吻合，并且可以在叶片组成化合物和相关光谱特征的上下文中理解。在这项研究中确定的长波高光谱带或特征有可能有助于将来对树种进行大规模的图像制图。建议采用集成策略对植被进行波段分析，以达到最高的准确性和稳定性。