## 4 Results and Analysis
### Results on AID Dataset  
In our experimentation, we employed a dataset comprising a total of 6000 images, with 4800 images designated for training purposes and the remaining 1200 images reserved for testing. Figure 7 shows the accuracy and loss curves for training and validation data from the AID dataset. The accuracy curves for both training and validation commenced at a modest 10% accuracy at the start of training, indicating a baseline level of recognition. As the training process unfolded, both curves exhibited a gradual yet consistent increase in accuracy. By the time epoch 11 was reached, the accuracy curves stabilized, with the validation accuracy reaching an impressive 98.02%. This high validation accuracy signifies the model's strong ability to accurately classify scenes across diverse categories. The stability observed in the curves persisted through epoch 13, with no significant fluctuations or improvements noted. Consequently, the decision was made to manually terminate the curve analysis at epoch 13 due to the sustained stability.  
Parallel to the accuracy curves, the loss curves, which illustrate the model's optimization progress, displayed a similar pattern. At the outset, both training and validation losses began at approximately 3%, reflecting a starting point for error. Subsequently, these losses gradually decreased as the model refined its recognition capabilities. By the time epoch 7 was reached, both training and validation losses had converted to a minimal value of 0%. This convergence underscores the model's effective learning process and its ability to minimize errors effectively. It is noteworthy that the remarkable training and validation performance was achieved efficiently, with the entire training process taking 149 min and 48 s. This efficiency further underscores the practical feasibility o  
Figure 6: Confusion matrix for our model on the scene classification dataset.  
model for real-world applications, as it combines both high accuracy and minimal loss in a relatively short time frame.  
Figure 8 shows the confusion matrix of the proposed method on the test sets of the second database. From the figure, we can observe that the Airport and Beach classes displayed a relatively high level of misclassification, with 27 out of 28 images for the Airport class and 13 out of 14 images correctly identified from the Beach class. However, one sample was incorrectly classified as Beach for the Airport class and was incorrectly classified as BaseballField for the Beach class. Further investigation may be required to address this misclassification. The Center class exhibited accurate classification for most samples, with 26 out of 28 samples correctly identified. Nevertheless, one sample was misclassified as BareLand and one sample was incorrectly classified as Beach. In the Church class, the model demonstrated strong performance, with 52 out of 54 samples correctly classified. Two samples, however, were misclassified as Desert and FarmLand. The Commercial class achieved excellent results, with 62 out of 64 samples correctly identified. Two samples were incorrectly classified as Meadow. Also, the Forest class achieved a good result with only two samples being incorrectly identified as Mountain. In the Parking and River classes, most samples, 61 out of 62 for Parking and 19 out of 20 for River, were correctly classified. One sample, however, was misclassified as Storage Tanks in both classes. The remaining classes were all classified correctly without any misclassifications.  
Figure 8: Confusion matrix for our model on the AID dataset.  
Figure 7: Training and validation progress of our method on the AID dataset.