Species composition of forests is a very important component from the point of view of nature conservation and forestry. We aimed to identify 10 tree species in a hilly forest stand using a hyperspectral aerial image with a particular focus on two invasive species, namely Ailanthus tree and black locust. Deep learning-based training data augmentation (TDA) and post-classification techniques were tested with Random Forest and Support Vector Machine (SVM) classifiers. SVM had better performance with 81.6% overall accuracy (OA). TDA increased the OA to 82.5% and post-classification with segmentation improved the total accuracy to 86.2%. The class-level performance was more convincing: the invasive Ailanthus trees were identified with 40% higher producer's and user's accuracies (PA and UA) to 70% related to the common technique (using a training dataset and classifying the trees). The PA and UA did not change in the case of the other invasive species, black locust. Accordingly, this new method identifies well Ailanthus, a sparsely distributed species in the area; while it was less efficient with black locust that dominates larger patches in the stand. The combination of the two ancillary steps of hyperspectral image classification proved to be reasonable and can support forest management planning and nature conservation in the future.

中文翻译：

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基于深度学习的训练数据增强结合后分类提高了优势和分散入侵森林树种的分类精度

从自然保护和林业的角度来看，森林的物种组成是一个非常重要的组成部分。我们的目标是使用高光谱航空图像识别丘陵林中的 10 种树种，特别关注两种入侵物种，即臭椿树和黑刺槐。使用随机森林和支持向量机 (SVM) 分类器测试了基于深度学习的训练数据增强 (TDA) 和后分类技术。SVM 具有更好的性能，总体准确率 (OA) 为 81.6%。TDA 将 OA 提高到 82.5%，并且通过分割进行分类后将总准确率提高到 86.2%。班级层面的表现更有说服力：与常见技术（使用训练数据集并对树木进行分类）相关，入侵臭椿树的生产者和用户准确度（PA 和 UA）提高了 40% 至 70%。其他入侵物种黑刺槐的 PA 和 UA 没有变化。因此，这种新方法可以很好地识别臭椿，这是该地区分布稀疏的物种；而对于占据林地较大斑块的刺槐来说，效率较低。高光谱图像分类的两个辅助步骤的结合被证明是合理的，可以支持未来的森林管理规划和自然保护。该地区物种分布稀疏；而对于占据林地较大斑块的刺槐来说，效率较低。高光谱图像分类的两个辅助步骤的结合被证明是合理的，可以支持未来的森林管理规划和自然保护。该地区物种分布稀疏；而对于占据林地较大斑块的刺槐来说，效率较低。高光谱图像分类的两个辅助步骤的结合被证明是合理的，可以支持未来的森林管理规划和自然保护。