## 5 Results
### Segmentation Quality Assessment
In Kocaeli, the highest overall accuracy result was found for the CNN (U-Net)-MRS model with five-band dataset and 32 \(\times\) 32 patch size as 98.44% (Table 10). The lowest accuracy result was found using the SegNet model 32 \(\times\) 32 patch size of the five-band dataset as 89.85% (Table 11). Likewise, in the accuracy assessment tables, the prediction figures found the recognizable differences in the results of the Kocaeli test site models with the five-band dataset (Figure 14). The harbor is generally minimalized or absent in semantic segmentation results with SegNet and CNN (SegNet)-MRS models. The most successful method in terms of overall accuracy for artificial surfaces and forest and semi-natural areas classes is CNN (U-Net)-MRS model using 32 \(\times\) 32 patch size. In the Kocaeli test site area, CNN (SegNet)-MRS models using 64 \(\times\) 64 patch size had the highest scores for all evaluation metrics (except IoU value of the forest and semi-natural areas class). Finally, through these results, it is revealed that the results of state-of-the-art models based on CNNs are improved by using the CNN-MRS model. The worst values were obtained with SegNet model with 32 \(\times\) 32 and 128 \(\times\) 128 patch sizes. Moreover, there is no optimum CNN-based model. It varies according to the dataset and test site area. The observable semantic segmentation results are shown for Kocaeli test site area with the seven-band dataset (Figure 15). There are considerable differences between this dataset and the five-band dataset for this region with the same models. In this experiment, our CNN (SegNet)-MRS model with 64 \(\times\) 64 patch size yielded superior results compared to other models. U-Net models obtained more detailed results, and this caused  
Figure 14: Kocaeli test area results of CNN models and CNN–MRS models with five-band dataset (**c–f**): \(32\times 32\) patch size; (**g–j**): \(64\times 64\) patch size; (**k–n**): \(128\times 128\) patch size. Figure 15: Kocaeli test area results of CNN models and CNN–MRS models with 7-band dataset (**c–f**): \(32\times 32\) patch size; (**g–j**): \(64\times 64\) patch size; (**k–n**): \(128\times 128\) patch size). The best scores are the results of the CNN-MRS models in both class metrics and the aggregate five-band dataset. With overall accuracy at 98.44% and kappa coefficient at 97.44%, the results of our approach with U-Net with 32 \(\times\) 32 patch size are the highest values (Table 9). For average F1-score, the 64 \(\times\) 64 patch size with the same model resulted highest at 96.46%. The lowest predicted results of these evaluation metrics were obtained by using SegNet model with the 32 \(\times\) 32 patch size (Table 11). For individual classes, F1-score and IoU results are shown as different accuracy evaluation metrics. According to all test results, for separate classes, the best scores belonged to CNN-MRS model performances. The CNN-MRS models were compared quantitatively with mainstream and state-of-the-art CNN-based models (U-Net and SegNet) (Tables 8-13). Significant superiority was shown by the CNN-MRS models, which had the best classification performances. The results of the seven-band dataset are shown in Tables 12 and 13. Applying a particular metric for understanding the assessment may not be sufficient in an imbalanced dataset. A more detailed evaluation may be required, especially for models with adaptation purposes. The results of the study were also examined as classification accuracy at the class level. Precision and recall of each class and recall values were calculated for all models with varied patch sizes for two datasets. Moreover, per-class F1-scores and average F1-score of the study also provide some required information about the efficiency of the models. In general, precision scores for the individual classes were found to be better than recall for both test site areas. In the Istanbul test area, the lowest precision values were found for SegNet using 128 \(\times\) 128 patch size in the five-band dataset.