# Application of Image Recognition Methods to Determine Land Use Classes
## 5 Discussion  
Finally, in this section, we summarize and discuss our obtained results regarding our previously defined research aim to improve the assessment of land cover changes by proposing a hybrid ML, cloud interpolation, and vegetation indices-based approach. As indicated by the findings of the literature review, the rapid development of artificial intelligence technologies and the increasing availability of high-resolution satellite data are enabling the efficient monitoring and management of land resources. However, the classification of land use remains challenging due to the variability of spectral characteristics, seasonal changes, regionality, and cloud interference. Consequently, the currently analyzed methods and approaches are unable to achieve the necessary level of accuracy when classifying land resources that are subject to dynamic change due to environmental conditions. This paper proposes a hybrid ML, cloud interpolation, and vegetation/water indices-based approach for the classification of Sentinel-2 satellite imagery. The proposed approach offers several key advantages. First, it allows the filtering of satellite images using the cloud percentage, cloud removal using cloud interpolation and spectral index. Second, a Random Forest algorithm is employed to assign land use labels. Finally, classification results are refined by filtering out low-confidence predictions and improving spatial consistency. The implemented approach and conducted experiments demonstrated that the hybrid approach significantly enhanced classification accuracy and robustness [4; 16; 22]. Our findings align with previous research [4; 16; 23], confirming the Random Forest classifier's suitability and its notable adaptability to spectral variability, seasonal changes, and common challenges such as cloud cover interference. It is important to note that cloud interpolation and vegetation or water indices (e.g., NDTI, INDVIre, and MNDWI) included in the proposed approach demonstrated improvement in classification reliability, echoing the results of previous studies that highlight the advantage of integrating spectral indices for more precise feature extraction from Sentinel-2 data [2; 5; 61]. Specifically, the implemented cloud interpolation method allowed us to effectively mitigate cloud interference, which was identified as a key factor for the improvements in the literature [6; 59; 69]. Moreover, the selected Random Forest classifier parameters--maximum tree depth set at 20, estimators at 100, min samples split at 2 and finally min samples leaf at 4 \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{3}{*}{**Land Use Class**} & \multirow{3}{*}{**CC**} & \multicolumn{8}{c}{**Month**} \\ \cline{3-8}  & & **April** & **May** & **June** & **July** & **August** & **September** & **October** \\ \cline{3-8}  & & **CV** & **CV** & **CV** & **CV** & **CV** & **CV** & **CV** \\ \hline \hline \multirow{3}{*}{
\begin{tabular}{c} Arabic land \\ Fallow \\ Stubble \\ Winter cereals \\ Intermediate crops \\ Intensive cultivated crops \\ Natural meadows \\ Forest \\ Stagnant Water \\ Urban areas \\ Sand dunes \\ Peatlands \\ \end{tabular} } & 11 & 0.3 & 0.32 & - & - & 0.26 & 0.38 & 0.32 \\ \cline{1-1}  & 12 & - & - & 0.41 & 0.43 & - & - & - \\ \cline{1-1}  & 13 & - & - & - & - & 0.47 & - & - \\ \cline{1-1}  & 14 & - & 0.3 & - & - & - & - & - \\ \cline{1-1}  & 15 & - & - & - & - & - & - & 0.46 \\ \cline{1-1}  & 16 & - & 0.37 & 0.49 & 0.4 & 0.55 & - & - \\ \cline{1-1}  & 10 & 0.36 & 0.36 & 0.29 & 0.29 & 0.45 & 0.46 & 0.55 \\ \cline{1-1}  & 13 & 0.3 & 0.39 & 0.39 & 0.31 & 0.46 & 0.4 \\ \cline{1-1}  & 10 & 0.25 & 0.25 & 0.25 & 0.34 & 0.25 & 0.25 & 0.25 \\ \cline{1-1}  & 10 & 0.43 & 0.43 & 0.58 & 0.68 & 0.39 & 0.63 & 0.68 \\ \cline{1-1}  & 10 & 0.54 & 0.54 & 0.48 & 0.48 & 0.25 & 0.3 & 0.5 \\ \cline{1-1}  & 16 & - & 0.5 & 0.39 & 0.39 & 0.39 & 0.53 & 0.3 \\ \hline \hline \end{tabular} (c)–Not analyzed. During different seasons not all classes appear, i.e., in June there is no stubble.  
\end{table}