Fine classification of vegetable crops covered with different planting facilities using UAV hyperspectral image
With large-scale and high-output values,the vegetable industry of China is a pillar industry to promote the income increase of farmers and the development of rural agricultural economy.Rapidly and accurately obtaining the structural information of vegetable crop planting is of considerable importance 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 fine classification of crops.However,vegetable crop planting scales and modes have considerable variations,and the fragmentation of agricultural landscape is high in China.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 introduced considerable challenges to the fine classification of vegetable crops.Hyperspectral images of Gaoqiao scientific research base of Hunan Academy of Agricultural Sciences were obtained by UAV.The field survey revealed that the area contains 14 ground feature categories,including eggplant,towel gourd,rice,pepper,and tomato.Support Vector Machine(SVM)is widely used in crop classification due to low requirements for data and excellent generalization capability.Meanwhile,deep convolution neural network structures can automatically learn the abstract features of images and obtain high-level and rich semantic information of samples to successfully complete the classification task.Therefore,SVM and Deep Learning(DL)methods were applied to the classification of vegetable crops in this study.Unlike other hyperspectral classification verification experiments that randomly select training sets,training and test samples were manually selected in this study to reduce the spatial correlation between training and test sets,and the performance of different classification methods was evaluated using confusion matrix.The results showed that based on hyperspectral images obtained by UAVs,the average overall accuracy of vegetable crop classification using SVM and DL methods is 78.03％and 90.75％,respectively,and the average Kappa coeffiicients are 0.7359 and 0.8887,respectively.Compared with the SVM methods,the fine classification effects obtained by the DL methods are more ideal.This finding is attributed to the effective extraction of spectral and spatial feature information from the image using the three-dimensional convolutional neural network and the convolutional neural network with attention mechanism,thus demonstrating a superior performance in the classification of vegetable crops.The spatial texture characteristics of vegetable crops are observed on large-scale plots,while they are various on small-scale plots.Thus,using different DL methods for the classification of vegetable crops on different scale plots is appropriate.Vegetable crops under different planting facilities were classified in this study using UAV hyperspectral images.Under the influence of complex backgrounds such as plastic films,bird nets,and greenhouses,satisfactory performance was still achieved using SVM and DL methods,which can provide technological support for the modernization,automation,and refinement of regional vegetable crop management.
fine classificationvegetable cropsUnmanned Aerial Vehicle(UAV)hyperspectralgreenhousesmulch film
万方数据
维普
