# Cross-Domain Transfer Learning for Natural Scene Classification of Remote-Sensing Imagery
## 4 Results and Discussion
### Fine-Tuning Only Last Three Layers of the Pre-Trained Model  
In this case, we only trained the last three layers of the ResNet-50 model on the NaSC-TG2 dataset using ImageNet weights as the starting point. We froze all previous layers and those layers acted as the feature extractor. The graphs of the model accuracy have been plotted for training vs. validation during the training of the last three layers of both ResNet-50 and VGG-19. These graphs are shown in Figures 12 and 13. By analyzing these graphs, it is evident that the training behavior of these models was quite similar to the previous experiment where we fine-tuned the entire models. We observed that the training of the ResNet-50 model was saturated after a few epochs while the VGG-19 model was slowly saturated with a lot of fluctuations along the way. After the training and evaluation, as shown in Figure 13, we achieved an overall accuracy of 99.10% and 97.28% for ResNet-50 and VGG-19, respectively. These results were slightly lower than in the previous case where the entire model was fine-tuned. However, these results were quite impressive because all the feature extraction parts of the network were frozen and only the classification part was fine-tuned on the NaSC-TG2 dataset. Detailed results for ResNet-50 indicating the evaluation metrics of the precision, recall, accuracy, and F1 score for each class in the dataset are shown in Table 2. We observed similar overall values for each evaluation measure because our dataset was evenly distributed among all the classes. Figure 11: Confusion matrix for fine-tuning entire model of ResNet-50. Figure 12: Training vs. validation accuracy curve during fine-tuning of last 3 layers of ResNet-50. Figure 13: Training vs. validation accuracy curve during fine-tuning of last 3 layers of VGG-19. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Class** & **Recall** & **Precision** & **Accuracy** & **F1 Score** \\ \hline Beach & 0.9925 & 1.0000 & 0.9992 & 0.9962 \\ \hline Circular farmland & 0.9950 & 0.9925 & 0.9988 & 0.9938 \\ \hline Cloud & 0.9975 & 0.9901 & 0.9988 & 0.9938 \\ \hline Desert & 0.9950 & 0.9950 & 0.9990 & 0.9950 \\ \hline Forest & 1.0000 & 1.0000 & 1.0000 & 1.0000 \\ \hline Mountain & 0.9825 & 0.9949 & 0.9978 & 0.9887 \\ \hline Rectangular farmland & 0.9800 & 0.9899 & 0.9970 & 0.9849 \\ \hline Residential & 0.9950 & 0.9925 & 0.9988 & 0.9938 \\ \hline River & 0.9800 & 0.9631 & 0.9942 & 0.9715 \\ \hline Snowberg & 0.9925 & 0.9925 & 0.9985 & 0.9925 \\ \hline Overall & 99.10\% & 99.11\% & 99.10\% & 99.10\% \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Last 3 layers of ResNet-50 Fine-tuned with NaSC-TG2. Figure 14 displays the confusion matrix for the fine-tuning of the last three layers of ResNet-50, and we can observe that the model performed admirably in all of the 10 classes, with no false negatives (FN) at all in the forest class, whereas the beach, circular farmland, cloud, and desert class have three false negatives. Rectangular farmland had eight false negatives which were misclassified as river class. River class had 15 false positives (FP) which was the highest FP for any class. Overall, fine-tuning the last three layers of the model resulted in an outstanding performance, surpassing many benchmarks. The results obtained using this technique were very close to the prior technique in which the entire model was fine-tuned. This is because these models were pre-trained on a very large dataset (i.e., ImageNet). Although the source domain was different to our applied domain and it contained natural images of regular objects, these models captured all the abstract and complex features in its feature extraction layers. We utilized these learned features for the remote-sensing domain by freezing those layers during the training. We fine-tuned only the final layers which captured the high-level features of the remote-sensing images. Since the low-level features are similar for all types of images, the deployed models were adapted easily to the NaSC-TG2 dataset for natural scene classification.