# Classification of Different Winter Wheat Cultivars on Hyperspectral UAV Imagery
## 2 Materials and Methods
### Data Processing
#### 2.3.1 Hyperspectral Imagery Pre-Processing  
The pre-processing of hyperspectral data acquired by UAV mainly includes three parts: radiation correction, imagery mosaicking, and spectral stitching. The radiation correction was completed by Matlab based on the center wavelength and half-width wavelength of the UHD185 [34]. Considering the limited hardware configuration of the data processing ground station and the large memory of the stitched full-band hyperspectral imagery, the data were firstly exported by JPG and cue formats based on Cubert-PILOT provided by Cubert. Firstly, aiming at mosaicking hyperspectral UHD185 imagery to be a hyperspectral map, this study used the Pansharpen algorithm to fusion the single panchromatic image of 1000 \(\times\) 1000 pixels and the corresponding hyperspectral image of 50 \(\times\) 50 pixels. Secondly, extracted and matched image feature points based on the improved SIFT (scale invariant feature transform) algorithm by PhotoScan software, and then used IDL (interactive data language) programming to realize the extraction and merging of hyperspectral sub-band imagery. Finally, the geocoding of hyperspectral imagery was completed based on ArcGIS to form a hyperspectral map, with a spatial resolution of 2.6 cm.  
Figure 2: UHD185 integrated hyperspectral equipment: (a) DJI M600 multi-rotor UAV (b) UHD185 Hyperspectral camera.  
This study then evaluated and analyzed the overall quality of both spatial information and spectral information of the pre-processed image, because both the spatial information and the spectral information played important roles in the following classification. In terms of spatial information, the pre-processed hyperspectral orthophoto image in Figure 4 was used to do the overall evaluation. It can be seen from Figure 4 that the hyperspectral imagery after stitching can effectively convey spatial geometry information, for example, the geometric position of its ground objects was stitched accurately, and the whole image was clear without stitched cracks.  
To evaluate the spectral information of the pre-processed imagery, five typical ground objects corresponding to the mosaicked hyperspectral orthographic image and the original hyperspectral image were selected, respectively. Their spectral data information was extracted and then analyzed and evaluated based on qualitative and quantitative methods.  
Figure 4: Hyperspectral orthophoto generated after imagery mosaicking and spectral stitching: (**a**) Panchromatic image; (**b**) Hyperspectral orthophoto image.  
Figure 3: Flowchart showing data processing methodology.  
Qualitative analysis was carried out by drawing the spectral curve of the extracted spectral data, and the results are shown in Figure 5. It can be seen from Figure 5 that the spectral curves of the selected ground objects of the original hyperspectral images are close to the spectral curves of the hyperspectral orthophoto after spectral curves stitching.  
Quantitative analysis was carried out by analyzing the correlation of the extracted spectral data, and the results are shown in Figure 6. The correlation coefficient of stitched spectral curves before and after mosaicking reached 0.97, and the imagery after mosaicking effectively retained the original spectral feature information.