## 4 Results and Discussions
### Comparing Integration Frameworks
#### 4.1.2 Comparing the Classification Methods Using Per-Class Accuracies  
Tables 6 and 7 present the per-class accuracies obtained for the assessed integration frameworks as well as the pixel ANN and the patch CNN.  
For the training area, the grassland was best classified by the patch CNN (0.93), followed by the pixel ANN (0.9) and patch filtering (0.88). Roads were best identified by the patch CNN (0.82). Patch filtering achieved a road classification accuracy of 0.76, which is worse than the methods, i.e., patch CNN and feature fusion. Patch filtering also performed best for the bare land class with an accuracy of 0.93. For the building class, feature fusion and decision fusion (0.85) outperformed the other methods. Patch CNN achieved the best results for dense vegetation or trees (0.92). The water class was best classified by the pixel ANN and feature fusion (0.94).  
For the test area, the grassland class was best classified by the patch CNN (0.88) followed by patch filtering (0.82). The OBIA ANN (0.55) and the decision fusion (0.60) achieved the worse results for the grassland class. OBIA ANN achieved the best results for the building class. In addition, bare land (0.89) was best classified by the patch filtering, and water (0.98) was best identified by the pixel ANN and decision fusion, respectively. Dense vegetation or trees were best classified with the patch CNN (0.91) and patch filtering (0.87).  
Moreover, each land cover type can be described with certain spectral and spatial features. The integrated frameworks depend either on image segmentation, deep feature  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{6}{c}{**Average Accuracy**} \\ \cline{2-7}
**Class Name** & **Pixel ANN** & **Patch CNN** & **OBIA ANN** & **Feature Fusion** & **Patch Filtering** & **Decision Fusion** \\ \hline Building & 0.81 & 0.84 & 0.81 & 0.85 & 0.79 & 0.85 \\ Road & 0.75 & 0.82 & 0.72 & 0.78 & 0.76 & 0.75 \\ Grass Land & 0.90 & 0.93 & 0.75 & 0.65 & 0.88 & 0.70 \\ Dense Vegetation or Trees & 0.85 & 0.92 & 0.72 & 0.74 & 0.89 & 0.64 \\ Water Body & 0.94 & 0.93 & 0.88 & 0.94 & 0.89 & 0.93 \\ Bare Land & 0.86 & 0.69 & 0.70 & 0.88 & 0.93 & 0.89 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Per-class accuracies obtained for each classification method using the training area dataset.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{6}{c}{**Training Area**} & \multicolumn{3}{c}{**Test Area**} \\ \cline{2-7}
**Model** & **OA** & **Kappa** & **F1-score** & **OA** & **Kappa** & **F1-score** \\ \hline Pixel ANN & 0.849 & 0.818 & 0.850 & 0.858 & 0.829 & 0.860 \\ Patch CNN & 0.897 & 0.860 & 0.900 & 0.898 & 0.876 & 0.890 \\ OBIA ANN & 0.838 & 0.763 & 0.840 & 0.842 & 0.784 & 0.830 \\ Feature Fusion & 0.869 & 0.807 & 0.870 & 0.860 & 0.788 & 0.850 \\ Patch Filtering & 0.919 & 0.868 & 0.920 & 0.917 & 0.872 & 0.910 \\ Decision Fusion & 0.864 & 0.793 & 0.850 & 0.862 & 0.793 & 0.850 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Accuracy assessment of the classification methods used in this research for the training and test areas.  
extraction, or both. That is why some methods may perform well on certain land cover types compared to other types. For example, with the feature fusion method, no segmentation effect appears in the classification results. This method, therefore, may not be suitable for urban land cover with the complex geometry of buildings that exist in the area.