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摘要： 利用无人机航拍获得光学影像数据，结合深度学习理论，建立树种识别模型，以期为大规模树种识别提供一种新的方式。首先以福建安溪县为例，采用无人机获取20 m及40 m高度的航拍影像。其次，以树种为对象，对航拍影像进行分割，获得12种树种影像。最后，结合深度学习理论，采用DenseNet卷积神经网络建立树种识别模型，探讨不同航拍高度以及不同网络深度对树种识别的影响。结果表明:不同航拍高度的树种识别模型，其分类精度均达80%以上，最高精度为87.54%。从航拍影像解析度分析，随着航拍影像解析度的下降，模型识别精度呈现下降趋势，以20 m航拍影像数据建构的树种识别模型，其分类精度高于40 m模型；从模型网络深度分析，随着模型网络层数的增加，模型分类精度出现下降现象，DenseNet121模型分类精度高于DenseNet169模型分类精度。综上所述，基于无人机航拍影像，结合深度卷积神经网络，提出了新的树种识别方式，并以安溪县森林树种识别为例证明了该分类框架的有效性。 关键词： 无人机;  深度学习;  树种识别;  光学影像     Abstract: To provide a new idea for large-scale tree species identification, the UAV is used to obtain optical images, and is associated with the theory of deep learning to establish tree species recognition models. First, the Anxi County in Fujian Province is taken as an example, UAV was photographed at different heights of 20 m and 40 m to obtain aerial images of trees. Second, using the tree species as the object, aerial images were segmented to obtain 12 species of tree images. Finally, combined with the deep learning theory, DenseNet is used to establish the tree species recognition model, and the effects of different aerial heights and different depths of network on tree species recognition are discussed. The classification accuracy of tree species identification models with different aerial heights reached more than 80%, and the highest precision was 87.54%. From the analysis of the resolution of aerial image, with the decline of the resolution of aerial image, the accuracy of model presented a downward trend. The tree species recognition model constructed with 20m aerial image data had a higher classification accuracy than the 40m model. From the depth analysis of the network, with the increase of the number of network layers of the model, the classification accuracy of the model decreased. The accuracy of the DenseNet121 model was higher than that of the DenseNet169 model. Based on UAV aerial images and combined with deep convolutional neural network, a new tree species identification method was proposed. The identification of forest tree species in Anxi County was used as an example to prove the validity of the classification framework.

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