# A Very High-Resolution Urban Green Space from the Fusion of Microsatellite, SAR, and MSI Images
## 3 Results  
### Classification Results and Model Performance  
Table 2 displays the overall accuracy values for the classification obtained from two different classifier algorithms. This table provides information on the selected features (bands) and their importance variables in each classifier. When utilizing only the PlanetScope dataset and the RF classifier algorithm, a good accuracy of 84.9% was achieved. However, when combining multiple images, the accuracy increased significantly to 95.9%. In the case of CART classifier algorithms, using only the PlanetScope dataset yielded an 85.1% accuracy, which increased slightly to 87.7% when multiple images were employed. When employing the RF classifier algorithm and a single PlanetScope dataset, the most crucial values were derived from B8 (NIR), B2 (Blue), and B7 (Red Edge). When using multiple images, the most crucial values were derived from band RTVI, followed by band B2 (Blue) of Sentinel-2, and band B1 (Coastal Blue) of Planet. In contrast, when using the CART classifier algorithm and a single PlanetScope dataset, the most crucial values were derived from B1 (Coastal Blue), B2 (Blue), and B3 (Green). When using multiple images, the most significant values were associated with band B2 (Blue) of Sentinel-2, followed by band B1 (Coastal Blue) of PlanetScope and band RTVI. The performance of the RF classifier algorithm showed that almost all the bands used made a significant contribution to the classification. In contrast, the CART classifier algorithm's performance indicated that only certain bands were significant contributors to the classification, while others were not. For instance, when using only PlanetScope bands, B1 (Coastal Blue) was significant, whereas the other bands were not. When utilizing multiple images, only B2 (Blue) from Sentinel-2 significantly contributed to the classification. Table 3 summarizes the details. The differences between RF and CART in the feature importance are because of five factors: algorithm differences, randomness in RF, correlation among features, model complexity, and interpretability. RF and CART are two different algorithms used for classification tasks. Random Forest is an ensemble learning method that builds multiple decision trees and merges their outputs to improve accuracy and generalize well on unseen data. CART, on the other hand, typically refers to a single decision tree. The differences in how these algorithms construct decision boundaries and handle feature interactions can lead to variations in feature importance. RF introduces randomness in the feature selection process by considering a random subset of features for each split in each decision tree. This random selection process can cause different subsets of features to be chosen as important across different trees, leading to a diverse set of feature importance rankings. In contrast, CART typically considers all features for each split, potentially leading to a more consistent feature importance ranking. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **RF** & \multicolumn{2}{c}{**CART**} \\ \cline{2-4}  & **PlanetScope Only** & **Multiple Images** & **PlanetScope Only** & **Multiple Images** \\ \hline Overall & & & \\ accuracy (\%) & 84.9 & 95.9 & 85.1 & 87.7 \\ \hline \multirow{4}{*}{Variable of importance} & \multirow{4}{*}{B2 (Blue), B8 (NIR), and B7 (Red Edge)} & B7 (Red Edge 3) of Sentinel-2, B1 (Coastal Blue) & B2 (Blue) of Sentinel-2, B1 (Coastal Blue), B2 (Coastal Blue) of of PlanetScope, and B12 (Blue), and B3 (Green) & B2 (Blue) of Sentinel-2 \\  & & & \\ \cline{1-1}  & & & \\ \cline{1-1}  & & & \\ \cline{1-1}  & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Classifier algorithm performance. Since PlanetScope and Sentinel-2 consist of multispectral bands, each capturing different aspects of the Earth's surface, some bands may be highly correlated with each other, meaning they convey similar information.