# Cross-Domain Transfer Learning for Natural Scene Classification of Remote-Sensing Imagery
## 4 Results and Discussion
### Fine-Tuning the Entire Pre-Trained Model  
To investigate the effect of all the weights of the entire pre-trained models on ImageNet, we fine-tuned all the weights of the entire pre-trained models on the dataset NaSC-TG2. For this, we used ImageNet weights as the starting point. Both ResNet-50 and VGG-19 were trained for 100 epochs and the plotted graphs of the model accuracy for training vs. validation are reported in Figures 9 and 10. The graph in Figure 9 shows that ResNet-50 started after a few epochs. This indicates that the pre-trained model has extensively learned abstract and complex features during its pre-training. While in Figure 10, the graph shows that VGG-19 does not converge in the early epochs and many fluctuations were observed during its training.  
After training and evaluation, ResNet-50 and VGG-19 achieved an impressive overall accuracy of 99.50% and 98.02%, respectively. This was a significant improvement over the performance benchmarks for this dataset. Detailed results for each class using the ResNet-50 model are shown in Table 1.  
Looking at the confusion matrix in Figure 11, we can observe that the model performed admirably in all of the 10 classes, with a perfect performance (with no false negatives) in some, e.g., circular farmland and rectangular farmland and an almost perfect performance in others, e.g., beach, cloud, forest, mountain, and residential, etc., with only one or two  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Class** & **Recall** & **Precision** & **Accuracy** & **F1 Score** \\ \hline Beach & 0.9950 & 0.9975 & 0.9992 & 0.9962 \\ \hline Circular farmland & 1.0000 & 0.9975 & 0.9998 & 0.9988 \\ \hline Cloud & 0.9975 & 0.9901 & 0.9988 & 0.9938 \\ \hline Desert & 0.9900 & 1.0000 & 0.9990 & 0.9950 \\ \hline Forest & 0.9975 & 0.9975 & 0.9995 & 0.9975 \\ \hline Mountain & 0.9975 & 0.9876 & 0.9985 & 0.9925 \\ \hline Rectangular farmland & 1.0000 & 1.0000 & 1.0000 & 1.0000 \\ \hline Residential & 0.9975 & 0.9901 & 0.9988 & 0.9938 \\ \hline River & 0.9900 & 0.9925 & 0.9982 & 0.9912 \\ \hline Snowberg & 0.9850 & 0.9975 & 0.9982 & 0.9912 \\ \hline Overall & 99.50\% & 99.50\% & 99.50\% & 99.50\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: Results for ResNet-50 when Entire Model is fine-tuned on NaSC-TG2.  
Figure 10: Training vs. validation accuracy curve during fine-tuning of entire VGG-19 model.  
Figure 9: Training vs. validation accuracy curve during fine-tuning of entire ResNet-50 model.  
false negatives (FN). The snowberg class had six false negatives which was highest recorded FN score for any class. The highest false positives (FP) score was five, for the mountain class.  
Overall, fine-tuning the entire model results in the highest accuracy as it optimizes the weights of the whole network according to the targeted domain. This is because we fine-tuned all the layers including the initial layers which capture low-level features and the final layers which capture high-level features. Since low-level features are similar for all types of images, only the final layers were adapted to the task of natural scene classification.