# Impact of Texture Information on Crop Classification with Machine Learning and UAV Images
## 3 Results and Discussion  
### Parameterization of RF and SVM Classifiers  
For the RF classifier, two parameters, the number of variables required for node partitioning and the number of trees to be grown, have to be selected. Firstly, the number of variables for node partitioning was set to \(\sqrt{n}\) of the total number of variables. For example, for the case using the August image with texture information, there were nine variables (three spectral bands and six texture features); thus, the number of variables for node partitioning was set to 3. To determine the number of trees to be grown, variations of OOB errors with respect to the number of trees were investigated. From the variations of OOB errors, one can judge whether a sufficient number of trees were used for the RF modeling. In general, the OOB errors tend to decrease as the number of trees increases, and then converge to a certain value at the specific number of trees. When six multi-temporal UAV images were used as inputs, no distinctive differences in OOB errors were observed, and the error values were also very low for different texture feature cases. Figure 3 shows the variations of OOB errors when using the August image without and with texture features. The four combination cases showed different convergence values, but the variation patterns were very similar. As the number of trees increased to about 50, the OOB errors of all four combination cases decreased sharply. Then, the OOB errors reached the convergence values when the number of trees was about 150. By considering the convergence of OOB errors and processing time, the number of trees to be grown was set to 150.  
Two parameters (cost and gamma) of the RBF kernel for the SVM classifier were tuned using a grid search. The optimal combination of the two parameters was determined through 10-fold cross-validation of training data. The optimal cost and gamma values were similar for combination cases of different kernel sizes of GLCM and input UAV images. Figure 4 presents the grid search results for the cases using the August image and six multi-temporal UAV images with texture feature GK31, showing the different training accuracy values. The training accuracy obtained by the grid search ranged between 52 and 82.4% for the case using the August image with tex  
Figure 3: Variations of out-of-bag (OOB) errors of RF models with respect to the number of trees for the case using the August UAV image without and with texture features: (**a**) visible and near infrared (VNIR) spectral bands only; (**b**) VNIR + 3 \(\times\) 3 kernel (GK3); (**c**) VNIR + 15 \(\times\) 15 kernel (GK15); and (**d**) VNIR + 31 \(\times\) 31 kernel (GK31).  
maximum training accuracy for the case using six UAV images increased to 94%. It should be noted that this accuracy was obtained during the training phase; hence, higher training accuracy may fail to achieve higher prediction performance. It was found that the performance difference with respect to variations of the model parameters for the SVM classifier was also great, compared to the RF classifier, which indicates the importance of optimal parameter search for the SVM classifier.