# Classification of Different Winter Wheat Cultivars on Hyperspectral UAV Imagery
###### Abstract  
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. Keywords:UAV; hyperspectral imagery; MNF; ENVINet5 model; winter wheat cultivar; intra-class difference +
Footnote †: journal: Applied Sciences
classification of crops mainly relies on morphological and biological investigation methods [8]. Although these methods have high accuracy, they are too time-consuming and laborious to be effectively applied on a large scale [9]. In recent decades, the rapid development of satellite remote sensing technology has provided new options for the classification of winter wheat. Classifying different cultivars of winter wheat is essentially a task about classifying features with weak intra-class variation in remote sensing imagery classification [10], since different cultivars of winter wheat are the sub-category of wheat, and they have similar phenotypes and consistent texture features [11]. Many studies used satellite data, such as Landsat, Modis, Sentinel, etc., to do the crop classification due to the wide coverage and low cost [12; 13; 14; 15]. Satellite remote sensing can collect data from a large spatial scale, but its practical application in fine-scale agricultural monitoring is limited by its long revisit periods and cloud occlusion [16; 17]. In addition, although there are improvements in the spatial resolution of the conventional multispectral remote sensing images, they have low spectral resolution [18]. These limitations are obvious in the classification of different crops which have similar spectral and texture information, such as different cultivars of winter wheat. UAVs are a quickly evolving technology and the use of UAV-based hyperspectral cameras in smart agriculture is currently an area of great research interest and development [19; 20; 21].