## 3 Results
### Evaluation by Channel Configuration  
As a result of the comprehensive analysis of the data presented in Table 4, six channel combinations RGB, NGR, RGBN, RGBN+NDVI, TOA, and TOA+NDVI were used to evaluate the performance of the cloud detection model. Performance was measured based on the precision, recall, F1-score, and mIoU and analyzed for four classes: clear sky, thick cloud, thin cloud, and cloud shadow, as well as for overall performance. The experimental results showed that for the clear sky class, all the channel combinations achieved F1-scores greater than 93%, with RGB (93.89%) and TOA (93.85%) demonstrating particularly strong performance. In the thick cloud class, TOA+NDVI achieved the highest F1-score at 64.97%, slightly outperforming RGBN (64.91%) and RGB (64.68%), while RGBN+NDVI (59.87%) showed relatively lower performance. In the thin cloud class, TOA+NDVI (46.62%) slightly outperformed RGB (45.97%) to achieve the best performance, while the other channels remained in the 38-42% range. In contrast, the cloud shadow class showed relatively high performance with NGR (38.61%) and RGBN (37.82%), while TOA+NDVI (31.75%) performed worse than TOA (34.87%) and RGBN+NDVI (32.39%). For overall performance, TOA+NDVI (59.27%) slightly outperformed RGB (59.11%) and showed superior results compared to the other combinations (56.04-58.80%). This indicates that TOA+NDVI provides a balanced performance across various classes. To statistically verify the provided experimental results, an ANOVA test was performed on the F1-scores of each class. The results showed F-statistics of 1228.9363 for Clear Sky_F1, 595.9905 for Thick Cloud_F1, 2164.7038 for Thin Cloud_F1, and 380.6453 for Cloud Shadow_F1, with corresponding _p_-values of 0.0000. This indicates statistically significant differences in the mean F1-scores across the channel combinations, allowing the rejection of the null hypothesis (H\({}_{0}\): no difference in mean F1-scores between all channels). The analysis was conducted at a 95% confidence level. Additionally, as shown in Table 5, post hoc pairwise comparisons using Tukey HSD (honest significant difference) revealed that TOA+NDVI showed significant differences from other channels in several classes. For instance, in Clear Sky_F1, the average difference between NGR and TOA+NDVI was 0.0072 (p-adj = 0.0), which was statistically significant but with a minimal practical difference. In Thick Cloud_F1, TOA+NDVI showed average differences of 0.0294 and 0.0136 higher than NGR and RGB, respectively (p-adj = 0.0). In Thin Cloud_F1, TOA+NDVI showed significant differences from most channels except RGB, where the average difference was 0.0017 \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Channel** & **RGB** & **NGR** & **RGBN** & **RGBN+NDVI** & **TOA** & **TOA+NDVI** \\ \hline Parameters & 45,670,484 & 45,670,484 & 45,673,620 & 45,676,756 & 45,673,620 & 45,676,756 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Parameter count for each input channel configuration. (p-adj = 0.7708), showing no significant difference. In Cloud Shadow_F1, TOA+NDVI showed average differences of \(-0.0517\) and \(-0.0312\) from NGR and TOA, respectively (p-adj = 0.0), reflecting relatively lower performance. These statistical analyses confirm that TOA+NDVI excels in the clear sky, thick cloud, and thin cloud classes but shows some limitations in the cloud shadow class.