5.1.2. Hierarchical Classification versus Classical Direct Extraction

The results of the hierarchical classification, which is the classification approach that was proposed in this study, and the classical direct extraction for the winter crops mapping are presented in Figure 8. Generally, winter wheat and winter barley were well detected and extracted from the Sentinel-2 image; the results of two classification approaches are globally identical, with particular reference to the homogeneous distribution of the winter crops over the area of interest. Nevertheless, the classical direct extraction approach identified more winter croplands, especially winter barley, and the winter croplands that were detected are much more fragmented; many small pixels were classified as croplands. This could be explained by the fact that winter crops are directly extracted from the preprocessed image; in addition, there might be some confusion between winter barley, grasslands, and some different crops considering the resemblance of their spectral behavior.

**Figure 8.** Classification results with hierarchical classification and classical direct extraction.

To make a better comparison, the accuracy assessments of the two approaches are displayed in Tables 8 and 9. According to the tables, both classification results are very satisfactory as mostly all of the accuracy indicators range from 0.8 to 1, specifically, with the hierarchical classification, almost all indices are superior to 0.9. This suggests a good performance and training of the models and also a strong agreement with ground truth of all classification approaches in the study. Still, it is worth noticing that the hierarchical classification shows a better potential for specific crop type mapping as compared to the classical direct extraction (approximately 0.1 higher in kappa and 0.07 in OA). Additionally, nearly every class achieved a higher accuracy in hierarchical classification, which indicates that the model is solid, and it is able to make a good prediction. Among the three classes, winter wheat is the most correctly classified class in both of the classification approaches, the indicators that range from 0.90 to 0.99 and with an F-score that is highly similar. Hierarchical classification reaches a better precision index, that means that the model is more exact, yet classical direction extraction achieved a finer recall, which means that the model returned more relevant results, and it can correctly and efficiently identify winter wheat. In addition, winter barley and the other classes were evaluated and less accurately classified, especially with the classical direct extraction approach. According to Table 8, the winter barley class obtained a high recall (0.960) and a relatively lower precision (0.683), which suggests a high false-positive rate; many individuals that were predicted as winter barley that the model returned were found misclassified when they were compared to the test data. On the contrary, the other class received a high precision index (0.955) and a comparatively low recall (0.797), and these indicators demonstrate that the pixels were correctly detected and labelled despite there being fewer results returned by the model. The comparably low accuracy of the two classes and the imbalance between the precision and recall indices might be explained by: (1) the similarity of the spectral behavior between winter barley and other crops and even grassland in the case of the classical direct extraction approach. (2) Since two winter crops are extracted directly from the Sentinel-2 image, the other class included not only non-vegetated urban areas, but also vegetation and other croplands which occupy a large area of our study site. Therefore, an imbalance between classes was caused, thus, more training datasets of the other class were acquired in consideration of its weak intraclass correlation.


**Table 8.** Accuracy assessment of hierarchical classification.

**Table 9.** Accuracy assessment of classical direct extraction.

