It is essential to classify tree species accurately for the sustainable management of forest resources and effective monitoring of species diversity. Airborne hyperspectral images have high spatial and spectral resolution, and consequently, the large quantity of information on spectral and spatial structures is effective for tree species classification. In this research, Gaofeng Forest Farm in Nanning, Guangxi Province, China, was used as the study site, and the airborne hyperspectral images were used as the data source. The spectral and textural information extracted by wavelet analysis and edge information extracted by mathematical morphological analysis composed a feature set. The feature set was filtered through a random forest, and object-oriented methods were used to classify tree species through a support vector classifier. The results showed that spectral features extracted by wavelet analysis were highly effective in classifying tree species that had the greatest spectral separability. Horizontal and vertical textures had no positive effect on the classification accuracy, while diagonal textures improved the classification accuracy of tree species. Texture features were not sensitive to stands with small areas and broken distributions, while the edge structure features extracted from mathematical morphology were sensitive to the complex forests. The overall accuracy of tree species classification by combining spectral, textural, and edge structural features was 96.54%, with a Kappa coefficient of 0.96. In the comparative test, the first-derivative and second-derivative of the hyperspectral image and texture features composed a feature set. Using the same classification methods, the OA was 80.91% and Kappa was 0.7711. Therefore, the wavelet analysis and mathematical morphology can significantly improve the tree species classification accuracy of hyperspectral images. Accurate tree species classification can provide basic scientific data for forest resource monitoring and management measures.

准确地对树种进行分类对于森林资源的可持续管理和有效监测物种多样性至关重要。机载高光谱图像具有很高的空间和光谱分辨率，因此，关于光谱和空间结构的大量信息对于树种分类是有效的。在这项研究中，以广西省南宁市的高丰林场为研究地点，以机载高光谱图像为数据源。小波分析提取的光谱和纹理信息以及数学形态分析提取的边缘信息组成了一个特征集。通过随机森林对功能集进行过滤，并使用面向对象的方法通过支持向量分类器对树木进行分类。结果表明，通过小波分析提取的光谱特征在光谱分离度最大的树种分类中非常有效。水平和垂直纹理对分类精度没有积极影响，而对角纹理则提高了树种的分类精度。纹理特征对面积小且分布破碎的林分不敏感，而从数学形态学提取的边缘结构特征对复杂的森林敏感。通过组合光谱，纹理和边缘结构特征进行的树种分类的整体准确性为96.54％，卡伯系数为0.96。在比较测试中，高光谱图像和纹理特征的一阶和二阶导数组成了一个特征集。使用相同的分类方法，OA为80.91％，Kappa为0.7711。因此，小波分析和数学形态学可以显着提高高光谱图像的树种分类精度。准确的树种分类可以为森林资源监测和管理措施提供基础科学数据。