# Impact of Texture Information on Crop Classification with Machine Learning and UAV Images
## 3 Results and Discussion
### Quantitative Accuracy Assessment  
The aforementioned visual and qualitative comparison results were further evaluated quantitatively by computing and comparing accuracy statistics. Confusion matrices were first prepared for all combination cases of each classifier, and related accuracy statistics were calculated by comparing classification results with reference data that were not used for training. Figure 5: Comparison of SVM-based classification results with ground-truth data: (**a**) ground-truth data; (**b**) August image with VNIR; (**c**) August image with VNIR and GK31 texture features; (**d**) six multi-temporal images with VNIR; and (**e**) six multi-temporal with VNIR and GK31 texture features. Figure 6: Overall accuracy of classification results without texture features and with texture features generated from different kernel sizes for the cases using the August image and six multi-temporal images. Figure 6 shows variations in OA of classification results without texture features (VNIR) and with texture features generated from different kernel sizes (GK3, GK15, and GK31) using the August image and multi-temporal images. Although a very small portion of ground-truth data were used as training data, the OA values for the two classifiers were notably high (i.e. over 97%) when multi-temporal images were used for classification. Regardless of the number of input images and the classifier type, the combination of texture features and spectral information led to an increase in OA. The OA also increased with kernel size; however, only a slight improvement of OA was achieved for classification with multi-temporal images as kernel size increased. This result can be explained by the fact that most useful information for the discrimination of crops was already provided by time-series spectral information; hence, the contribution of texture features was minimal. In contrast, the improvement in OA by accounting for texture features was much more significant in the classification result using the August image only than using the multi-temporal images. Furthermore, the kernel size of GLCM greatly affected the OA using the August image. As kernel size increased, OA increased for both SVM and RF classifiers, and the use of GK31 texture features showed the best classification accuracy. When comparing classification performance of both classifiers, the SVM classifier exhibited better OA than the RF classifier for the classification with the August image, indicating the superiority of the SVM classifier for the classification of crops in this study area. The difference in OA between SVM and RF classifiers was significant at the 5% significance level from the McNemar test [49], regardless of kernel sizes. It is noteworthy that the small difference in OA between two classifiers was significant at the 5% significance level even for all classification results based on multi-temporal images. Despite the similar OA values between two classifiers in the classification of multi-temporal images, this statistically significant difference was mainly due to evaluation with a very large amount of reference data (6,710,210 pixels). Even though parameter tuning is more demanding in the SVM classifier than the RF classifier, the optimal two parameters of the SVM classifier which were determined during a training stage with a relatively small training dataset could avoid overfitting the training data, leading to generalization ability for the large amount of reference data in this study. Some confusion matrices for typical combination cases of the SVM classifier (one image versus multi-temporal images and with or without texture features) are listed in Table 3. Considering only the August image, combining texture features (GK31) with spectral information led to an increase of 7.72%p in OA, compared with the classification result with spectral information only (from 83.13% to 90.85%). The increase of class-wise accuracies was also achieved, as well as the improvement in OA.