Classification of Different Winter Wheat Cultivars on Hyperspectral UAV Imagery

Crop phenotype observation techniques via UAV (unmanned aerial vehicle) are necessary to identify different winter wheat cultivars to better realize their future smart productions and satisfy the requirement of smart agriculture. This study proposes a UAV-based hyperspectral remote sensing system for the fine classification of different winter wheat cultivars. Firstly, we set 90% heading overlap and 85% side overlap as the optimal flight parameters, which can meet the requirements of following hyperspectral imagery mosaicking and spectral stitching of different winter wheat cultivars areas. Secondly, the mosaicking algorithm of UAV hyperspectral imagery was developed, and the correlation coefficient of stitched spectral curves before and after mosaicking reached 0.97, which induced this study to extract the resultful spectral curves of six different winter wheat cultivars. Finally, the hyperspectral imagery dimension reduction experiments were compared with principal component analysis (PCA), minimum noise fraction rotation (MNF), and independent component analysis (ICA); the winter wheat cultivars classification experiments were compared with support vector machines (SVM), maximum likelihood estimate (MLE), and U-net neural network ENVINet5 model. Different dimension reduction methods and classification methods were compared to get the best combination for classification of different winter wheat cultivars. The results show that the mosaicked hyperspectral imagery effectively retains the original spectral feature information, and type 4 and type 6 winter wheat cultivars have the best classification results with the classification accuracy above 84%. Meanwhile, there is a 30% improvement in classification accuracy after dimension reduction, the MNF dimension reduction combined with ENVINet5 classification result is the best, its overall accuracy and Kappa coefficients are 83% and 0.81, respectively. The results indicate that the UAV-based hyperspectral remote sensing system can potentially be used for classifying different cultivars of winter wheat, and it provides a reference for the classification of crops with weak intra-class differences.