## 5 Experimental Results and Discussion
### Classification  
Performance of the classifier was evaluated by user's accuracy (UA), producer's accuracy (PA), overall accuracy (OA), and the kappa coefficient (K) shown in the confusion matrix see Table 4, which is the most common metric that has been used for classification tasks [19; 75; 76; 77]. Overall accuracy indicates the overall performance of our proposed Pixel R-CNN architecture by calculating a ratio between the correctly classified total number of pixels and total ground truth pixels for all classes. The diagonal elements of the matrix represent the pixels that were classified correctly for each class. Individual class accuracy was calculated by dividing the number of correctly classified pixels in each category by the total number of pixels in the corresponding row called User's accuracy, and columns called Producer's accuracy. PA indicates the probability that a certain crop type on the ground is classified as such. UA represents the probability that a pixel classified in a given class belongs to that class. Our proposed pixel-based Pixel R-CNN method achieved OA = 96.5% and Kappa = 0.914 with 15 number of classes for a diverse large scale area, which exhibits significant improvement as compared to other mainstream methods. Water bodies and trees stand highest in terms of UA with 99.1% and 99.3%, respectively. That is mainly due to intra-class variability and the minor change of NIR band reflectance over time, which was easily learned by our Pixel R-CNN. Most of the classes, including the major types of crops such as Maize, Wheat, Lucerne, Vineyard, Soya, Rye, and Barley, were classified with more than 95% UA. Grassland being the worst class, which was classified with the PA = 65% and UA = 63%. The major confusion of grassland class was with Lucerne and Vineyard. It is worth mentioning that the Artificial class, which belongs to roads, buildings, urban areas, represents the mixed nature of pixel reflectances and was accurately detected with UA = 97% and PA = 99%. For class Apple, obtained PA was 86% while UA = 68%, which shows that 86% of the ground truth pixels were identified as Apple, but only 68% of the pixels classified as Apple in the classification actually belonged to class Apple. Some Pixels (see Table 4) belongs to Peer and Vineyard were mistakenly classified as Apple. The final classified map is shown in Figure 11 with the example of the zoomed part and the actual RGB image. To the best of our knowledge, a multi-temporal benchmark dataset is not available to compare classification approaches on equal footings. There are some data sets available online for crop classification without having ground truth of other land cover types such as Trees, Artificial land (build ups), Water bodies, Grassland. Therefore it is difficult to compare classification approaches on equal footings. Indeed, a direct quantitative comparison of the classification performed in these studies is difficult due to various dependencies such as the number of evaluated ground truth samples, the extent of the considered study area, and the number of classes to be evaluated. Nonetheless, we provided an overview of recent studies and their performances of the study domain by their applied approaches, the number of considered classes, used sensors, and achieved overall accuracy in Table 5. [PERSON] et al. [30], achieved 89% OAA by using RF classifier on the extracted phenological features from MODIS time-series data. They determined that good classification accuracies can be achieved with handcrafted features and classification algorithms if the temporal resolution of the data is sufficient. Though, the MODIS sensor data is not suitable for classification of the areas of large homogeneous regions due to its low spatial resolution (500 m). [PERSON] et al. [78] used high spatial resolution data from the RapidEye sensor and achieved 90% OAA for nine considered classes.