我国蔬菜产业规模大、产值高，是促进农民增收和农村农业经济发展的支柱产业。快速准确地获取区域尺度蔬菜种植结构信息对于农业现代化、自动化和精细化等具有重要意义。无人机高光谱遥感技术具有快速机动灵活和“图谱合一”的优势，在作物精细分类中具有广泛应用前景。然而蔬菜作物种植规模差异大、农业景观破碎度高，同时还受地膜、大棚和防鸟网覆盖等影响，无人机高光谱图像易产生严重的混合光谱效应，给蔬菜作物精细分类带来了极大的挑战。针对此问题，本研究以湖南省农科院高桥科研基地蔬菜种植区为例，获取无人机高光谱图像，探索采用支持向量机和深度学习方法对不同蔬菜作物进行精细分类。研究结果表明：基于无人机高光谱遥感数据，可以实现不同覆盖背景下的蔬菜作物精细分类；两大分类方法的平均总体精度分别为78.03%和90.75%，平均Kappa系数分别为0.7359和0.8887，相较于支持向量机方法，基于深度学习的分类方法获得的精细分类效果更加理想，三维卷积神经网络和引入注意力机制的卷积神经网络可以有效提取图像中的光谱-空间特征信息，在蔬菜作物精细分类中体现出更好的分类效果；蔬菜作物在大尺度地块上空间纹理特征明显，而在小地块尺度上差异较大，宜采用不同深度学习方法对其进行精细分类；不同覆盖背景对蔬菜作物产生混合光谱效应，对作物精细分类效果影响显著。

Objective：With large scale and high output value, China"s vegetable industry is a pillar industry to promote the increase of farmers" income and the development of rural agricultural economy. Rapidly and accurately obtaining vegetable crops planting structure information is of great significance for agricultural modernization, automation and precision. With the advantages of fast mobility, flexibility and image-spectrum merging, Unarmed Aerial Vehicle (UAV) hyperspectral remote sensing has wide prospects in crops fine classification. However, there are great variations in vegetable crop planting scales and modes and the fragmentation of agricultural landscape is high in China, and the vegetable crops are also affected by the coverage of plastic film, greenhouse and bird proof net, which easily produced the mixed spectral effect in UAV Hyperspectral images and also brings big challenges to the fine classification of vegetable crops. Method：Hyperspectral images of Gaoqiao scientific research base of Hunan Academy of Agricultural Sciences were obtained by UAV. According to the field survey, the area contains 14 ground feature categories, including eggplant, towel gourd, rice, pepper, tomato, and etc. Due to low requirements for data and excellent generalization ability, Support Vector Machine (SVM) is widely used in crops classification. And deep convolution neural network structure can automatically learn the abstract features of images and obtain higher-level and richer semantic information of samples, so as to better complete the classification task. For these reasons, SVM and Deep Learning (DL) methods were applied to the classification of vegetable crops in this study. Different from many hyperspectral classification verification experiments that randomly select training sets, training samples and test samples were manually selected in this study to reduce the spatial correlation between training sets and test sets. And the performance of different classification methods was evaluated using confusion matrix. Result：The results showed that using hyperspectral images obtained by UAV, the average overall accuracy of vegetable crops classification using SVM and DL methods is 78.03% and 90.75% respectively, and the average Kappa coefficients is 0.7359 and 0.8887 respectively. Compared with the SVM methods, the fine classification effects obtained by the DL methods are much more ideal, which is because the three-dimensional convolutional neural network and the convolutional neural network with attention mechanism can effectively extract the spectral spatial feature information in the image, thus shows a better performance in the classification of vegetable crops. The spatial texture characteristics of vegetable crops are obvious on large-scale plots, while they are various on small-scale plots, thus it is appropriate to use different DL methods for classification of vegetable crops on different scale plots. Conclusion：In this study, vegetable crops under different planting facilities were classified using UAV hyperspectral images. Under the influence of complex background such as plastic film, bird net and greenhouse, good performance was still achieved using SVM and Deep Learning (DL) methods, which can provide technology support for the modernization, automation and refinement of regional vegetable crop management.