## 5 Discussion  
The results of our scene recognition model demonstrate its remarkable performance in accurately classifying scenes across multiple diverse categories. The core of our methodology revolves around the innovative concept of integrating multiple modalities into a single input for scene recognition. This approach significantly deviates from traditional models that predominantly rely on processing data from a single modality. To further optimize the feature representations resulting from the multimodal fusion, we introduced the technique of _MI_-based feature selection. This novel approach improves the quality, discriminative capability, and resilience of the features, thus providing significant advantages for scene identification tasks. This innovation is a key contribution to our model, as it enhances the effectiveness of feature representations. In the first experiment conducted on the Scene Classification dataset, which consists of six scene classes (Buildings, Forests, Glacier, Mountains, Sea, and Street), our model achieved outstanding results. In the second experiment conducted on the AID dataset, which consists of 30 different scene classes, our model also achieved good results compared to other methods using the same dataset. Table 3 shows a summary of the total performance of our method on the two datasets. These results illustrate the model's capacity to adapt and effectively handle a diverse array of scenario categories. From Figures 5 and 7, the accuracy and loss curves depict the successful training and validation of our scene recognition model. The stability achieved in both accuracy and loss, along with the rapid convergence to high accuracy and minimal loss values, highlights the effectiveness of our approach in accurately classifying scenes. Moreover, the efficient training process underscores the practical feasibility of our model for real-world applications. From Figures 6 and 8, the confusion matrices underscore the exceptional classification performance of our scene recognition model. The absence of misclassifications within the matrix highlights the model's precision and effectiveness in identifying diverse scenes, making it a robust and reliable tool for scene recognition tasks. In comparison to previous studies in the field of scene recognition, our model stands out due to its multimodal approach and the innovative feature selection technique shown in Table 4. Traditional unimodal methods have been surpassed by our approach, which leverages the power of multimodal fusion directly on the same input image data. This advancement is crucial for applications such as autonomous vehicles, intelligent surveillance systems, and augmented reality, all of which rely on comprehensive scene understanding and interpretation. From the table, we can observe that our approach demonstrates superior performance compared with other previous methods for scene recognition. Compared with the method presented by [PERSON] et al. [13], our accuracy is slightly lower at 98.83%, but it is still competitive, considering the exceptional performance of their approach. Moreover, our method may offer advantages in terms of complexity and resource requirements. Finally, our approach's ability to combine CNN with _MI_ contributes to its superior performance, making it a promising solution for scene recognition tasks. We aim to highlight that the extraordinary performance of our model may be attributed to our creative approach, rigorous methodology, and the integration of feature selection based on _MI_. Our model has been meticulously crafted to possess both robustness and efficacy in the classification of situations over a wide range of categories. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Database** & **Accuracy (\%)** & **Precision (\%)** & **Recall (\%)** & **F1 Score (\%)** \\ \hline Scene Classification [28] & 100 & 100 & 100 & 100 \\ \hline AID [29] & 98.83 & 98.83 & 98.83 & 98.83 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Overall performance of our method on the two datasets.