## 3 Results  
### Comparison of Class Homogeneity of Two Regions  
Prior to comparing the classification results, the class homogeneity of the two study areas was first compared to quantify the different class compositions and landscape heterogeneity that significantly affect the classification accuracy.  
Figure 5 presents the variations in GCH and CV values with respect to different training patch sizes. For a fair comparison of GCH and CV values in the two study regions, only three patch sizes applied to the Illinois region were considered for the Anbandegi region. The GCH values in Anbandegi were higher than 0.9 for all patch sizes (Figure 5a), signifying the homogeneous distribution of crop classes in Anbandegi. As the patch size increased, the GCH value decreased, but the difference in the GCH value with respect to the patch size was very small (less than 0.1). Because ultra-high spatial resolution UAV imagery was used for classification, each crop contained a large number of pixels with the same crop type. Consequently, most of the patches were likely to be homogeneous in terms of class composition.  
In contrast, the Illinois region exhibited GCH values between 0.5 and 0.8, implying a relatively heterogeneous class distribution, compared with Anbandegi. The relationship between patch size and GCH value in the Illinois region was similar to that in Anbandegi: the larger the patch size, the smaller the GCH value. However, the difference in the GCH value with respect to the patch size was approximately 0.3, which was larger than in Anbandegi. This indicates that the class composition within a patch in the Illinois region became more complex or heterogeneous as the patch size increased. The area extent of crop areas in Illinois was much larger than that in Anbandegi, but the number of pixels comprising crop parcels was small in the coarse resolution Landsat images. Furthermore, mixed pixels containing boundaries either between crop parcels or between crop and non-crop areas included in the images. Hence, many pixels having different classes existed around the center pixel within a patch, thereby resulting in heterogeneous class compositions within the patches in the Illinois region.  
Analyzing the CV values (Figure 5b), those for the two regions increased accordingly as the patch size increased. However, the CV value in Anbandegi was much smaller than it was in Illinois, regardless of the patch size. Furthermore, the difference in CV for different patch sizes was small (0.05) in Anbandegi, compared with that in Illinois (0.11). The small CV value in Anbandegi implies small variations in LCH within the patch across the entire study area. By contrast, the larger CV value in the Illinois region indicates that variations in LCH within the patches were more prominent across  
Figure 5: Variation in (**a**) global class homogeneity (GCH) and (**b**) coefficient of variation of class homogeneity (CV) of two study regions.  
the study area. The relatively larger CV value for the larger patch size was due to a decrease in class homogeneity by the inclusion of more pixels of different classes in a large patch. These significant differences in class homogeneity of the two regions imply that the class purity of the training patches will exert different effects on the classification performance.