# Classification of Different Winter Wheat Cultivars on Hyperspectral UAV Imagery
###### Abstract
Due to the low flying height and limited ground coverage, UAV-based hyperspectral cameras can obtain images with a resolution of 2-5 cm or less, which makes these images have both high spatial and spectral resolutions [22; 23; 24]. In addition, it can be used to monitor areas which are difficult for humans to reach. Thus, these images are widely used to detect the subtle spectral differences between crops and provide a unique data source for the precise classification of crops with similar spectral curves. For example, [PERSON] et al. utilized UAV-based multi-angle hyperspectral remote sensing to obtain the fine classification of rich vegetation types, and the classification results based on multi-angle hyperspectral data were improved (OA = 89.2%, kappa = 0.870) compared with results only based on digital orthophoto maps (OA = 39.8%, kappa = 0.301) [25]; [PERSON] et al. employed UAV-based hyperspectral datasets to identify different crops in cities of Hanchuan and Honghu in China, and their proposed classification method can effectively improve the classification accuracy, protect the edges and shapes of the features, and relieve excessive smoothing while retaining detailed information [26]; [PERSON] et al. used UAV hyperspectral imagery to evaluate the impact of different spatial resolutions on the identification of different crop types in highly fragmented planting areas. Their study also proved that the appropriate feature parameters vary at different scales, thus it was important to select appropriate feature parameters for imagery in different scales based on the different classifying tasks and user requirements [27]. These reviewed studies provide some ideas for the classification of UAV-based hyperspectral imagery. However, their research subjects are mainly about different kinds of crops. Few studies focus on the classification of UAV-based hyperspectral imagery about the same crop with different cultivars. Although UAV-based hyperspectral technology has unique advantages and can make full use of ground object information to extract and classify ground objects, there are several critical problems in processing hyperspectral data: (1) Curse of dimensionality, due to the large number of spectral channels [28]. A large number of channels and redundant information between different channels easily cause dimension disaster. For hyperspectral data, the higher spectral dimension does not equal the higher classification accuracy. Thus, it is necessary to effectively reduce the data dimension and unnecessary information during hyperspectral data processing [29]. (2) Mixed pixel phenomena [30]. The increased spatial resolution makes hyperspectral data contain more detailed features, resulting in spectral changes and heterogeneity within the same feature. This characteristic reduces the spectral separability and increases the salt-and-pepper classification noise [31]. This is a major interference when classifying data with complex feature types, such as the classification of urban features. (3) The limited number of labeled training samples [32]. In classifying hyperspectral imagery, it is difficult to label samples, and there are limited available training samples. (4) The selection of appropriate classification methods [33]. There are many classification methods for hyperspectral data. It is necessary to select appropriate methods to improve classification accuracy based on the different classifying tasks and user requirements. In this context, this study aimed to improve the classification accuracy of different varieties of winter wheat, which have weak intra-class differences. The wheat research base at Wuzhi county, Henan province, was taken as the experimental area. Firstly, this study integrated DJI M600 UAV (DJI Technology Co., Shenzhen, China) and UHD185 hyperspectral camera (Cubert GmbH, Ulm, Germany) into a hyperspectral remote sensing system to get hyperspectral imagery of winter wheat in the study area.