Croplands Extraction

Subsequently, based on the vegetated area that was extracted from the previous step, we aimed to distinguish and preserve only the croplands from all arboreal vegetation, shrub, and grasslands, including pasture. In this step, OBC and PBC were both performed and evaluated. Figure 6 demonstrates that the results of the two methods are almost identical, although more individual pixels were classified as cropland in PBC considering that PBC was operated on pixel-level.

**Figure 6.** Level 2: PBC and OBC croplands extraction results.

Pursuant to Tables 4 and 5, even though the global accuracy indices of the results of OBC are slightly better than PBC with a difference of 0.024 in kappa and 0.004 in OA, the indices of the two results are still comparable. The tables below show that a large proportion of pixels are correctly predicted, in general, and that the level of agreement with the ground truth data is somewhat lower, but it is still acceptable. Furthermore, for the

interclass accuracy evaluation, cropland generally has the highest precision, recall, and F-score results, which are all around 0.90. The models were well trained to make a good prediction of the cropland class, especially for the OBC model, and most of the individual pixels belonging to the cropland class were correctly detected. This can be explained by the OBC taking into account the geometry, form, and texture elements, which are the key elements that are used to distinguish the croplands from other vegetation. The classification of the vegetation has a slightly lower accuracy of approximately 0.2 in comparison with croplands because of the mix of different kinds of vegetation and the uncertain form of the vegetated area, though OBC remains more precise when it is compared to PBC. Finally, the classes of the other pixels in our study area, which were mainly some isolated pixels that were left from the previous step due to some errors, were better classified with PBC since the non-vegetated area has highly different spectral behavior as compared to that of vegetation. Considering the better accuracy assessments of OBC, its classification result was preserved to perform the next step of classification.

**Table 4.** Accuracy assessment of OBC croplands extraction.


**Table 5.** Accuracy assessment of PBC croplands extraction.


#### Winter Crops Extraction

In this final step, two winter crop types were extracted, based on the results of the previous step, and the classification result of the cropland extraction was achieved by using OBC. The results of the two classification methods (Figure 7) are very close to identical in this level, wherein the differences between the two maps can hardly be noticed.

**Figure 7.** Level 3: PBC and OBC winter crops extraction results.

With the lack of a possibility to visually compare the two methods, they were evaluated and compared by using accuracy assessments (Tables 6 and 7). In regard to the global accuracy indices, all classes were stated as accurate when using the two methods, which signifies a good performance by both methods with a high accuracy and a strong level of agreement for the classification. Beyond that, it is worth noticing that PBC shows a better potential with about 0.03 higher value in the OA and 0.04 in kappa, moreover, PBC basically achieves a better accuracy indicator of three classes in comparison with that of the OBC. The results illustrate that the difference in spectral behavior was exploited to distinguish winter crops from other crops, since all the croplands share similar geometry, form, and texture characteristics. Nonetheless, among the different crop types that were presented in our area of study, winter wheat has the most distinctive spectral signature, thus it was found to be the class with the best accuracy indices in both results, with very strong reliability in terms of prediction and a high rate of precisely identifying winter wheat. In contrast, the classification of winter barley and other crops are somewhat less accurate with approximately 0.1–0.5, and the advantage of PBC is more significant, with higher accuracy indicators of around 0.04, which might be caused by the confusion of winter barley and other crops due to the similarity of their spectral behavior. In addition, the difference between these two classes were better detected by PBC with spectral information.




