# Application of Image Recognition Methods to Determine Land Use Classes
## 4 Results
### Classification Results and Reached Accuracy
#### 4.3.2 Final Classification Results  
One of the key additional findings was that the inclusion of cloud interpolation significantly improved classification consistency in cloud-prone areas. The ability to reconstruct missing data from temporal information allowed the model to maintain high accuracy even in challenging atmospheric conditions. This approach ensured that cloud cover did not significantly affect classification results, making the method more suitable for operational land monitoring.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Classifier** & **Cohen’s Kappa** & **F1-Score** & **Recall** & **Precision** \\ \hline RF & 89.23\% & 90.53\% & 90.86\% & 90.21\% \\ SVM & 86.23\% & 87.93\% & 88.16\% & 87.71\% \\ KNN & 84.73\% & 85.08\% & 85.36\% & 84.81\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: Classification performance metrics for evaluated algorithms.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Classifier** & **Optimal Hyperparameters** \\ \hline RF & _n_estimators_ = _100_, _max_depth_ = _20_, _min_samples_leaf_ = \(4\), \\  _min_samples_split_ = \(2\) \\ SVM & \(C\) = _0.1_, _gamma_ = _scale_, _kernel_ = _rbf_ \\ KNN & _n_neighbors_ = _10_, _weights_ = _uniform_, \(p\) = \(2\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Optimal hyperparameters for each classification algorithm.  
Additionally, the proposed method significantly outperformed traditional classification approaches by reducing misclassification due to seasonal variations in land use. Traditional methods often struggle with land cover transitions that occur over time, such as crop growth and deforestation. By incorporating vegetation indices and cloud handling mechanisms, the developed classification approach improved the differentiation of land use classes over multiple seasons, minimizing classification errors caused by spectral similarities between different land types. The input and output examples of the satellite image classification are shown in Figure 11.  
Computational performance was also evaluated, showing that the classifier was efficient in processing large datasets while maintaining accuracy. Increasing the number of estimators in the Random Forest model improved classification performance but also increased processing time. A balance was achieved by optimizing hyperparameters to ensure both computational feasibility and classification accuracy. Further analysis of confidence maps revealed that some land use classes had higher classification uncertainty, especially in cases of spectral overlap. Refining the training datasets and incorporating additional contextual information could further improve the model's performance in these ambiguous cases. The final results obtained are presented in Table 5.  
Figure 11: Input and output examples of the satellite image classification. (**a**) Input satellite image for the classification algorithm; (**b**) output satellite image of the classification algorithm (note: the legend presents only land use classes which appear in the analyzed region).  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Month** & **OA** & **Precision** & **Recall** & **F1** & **Cohen’s Kappa** \\ \hline April & 90.45\% & 92.69\% & 91.45\% & 92.07\% & 90.96\% \\ May & 91.67\% & 92.28\% & 90.59\% & 91.43\% & 90.05\% \\ June & 90.40\% & 93.55\% & 90.40\% & 91.95\% & 88.50\% \\ July & 93.97\% & 97.20\% & 92.97\% & 95.04\% & 93.62\% \\ August & 90.61\% & 91.10\% & 90.61\% & 90.85\% & 90.03\% \\ September & 90.06\% & 96.26\% & 90.06\% & 93.06\% & 89.69\% \\ October & 93.13\% & 93.43\% & 93.13\% & 93.28\% & 90.13\% \\ \hline \end{tabular}
\end{table}
Table 5: Experiment accuracy results (overall accuracy (OA)).  
As shown in Table 5, the proposed method guarantees a classification accuracy of more than 90%, except for the months of June and September, where the results of the Cohen Kappa index indicate an accuracy of less than 90%. However, this deficiency is negligible.