# Cross-Domain Transfer Learning for Natural Scene Classification of Remote-Sensing Imagery
## 4 Results and Discussion
### Summary of Results  
ResNet50's residual connections contribute to its enhanced generalization ability. The skip connections help in the flow of information, enabling the model to retain relevant information from earlier layers, even in deeper layers. This allows ResNet50 to capture both low-level and high-level features, leading to an improved performance in remote-sensing image classification tasks. VGG19, on the other hand, relies solely on sequential convolutional layers, which may not capture information as effectively across different scales. A comparison of the results for both ResNet-50 and VGG-19 are summarized in Table 4.  
Figure 17: Confusion matrix for training of ResNet-50 from scratch. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Class** & **Recall** & **Precision** & **Accuracy** & **F1 Score** \\ \hline Beach & 0.9400 & 0.8931 & 0.9828 & 0.9160 \\ \hline Circular farmland & 0.9050 & 0.8538 & 0.9750 & 0.8786 \\ \hline Cloud & 0.8475 & 0.7402 & 0.9550 & 0.7902 \\ \hline Desert & 0.9200 & 0.9608 & 0.9882 & 0.9400 \\ \hline Forest & 0.9250 & 0.9763 & 0.9902 & 0.9499 \\ \hline Mountain & 0.855 & 0.7755 & 0.9608 & 0.8133 \\ \hline Rectangular farmland & 0.8675 & 0.6648 & 0.9430 & 0.7527 \\ \hline Residential & 0.8700 & 0.9039 & 0.9778 & 0.8866 \\ \hline River & 0.4350 & 0.6021 & 0.9148 & 0.5051 \\ \hline Snowberg & 0.7150 & 0.9597 & 0.9685 & 0.8195 \\ \hline Overall & 82.80\% & 83.30\% & 82.80\% & 82.52\% \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Results for training of ResNet-50 on NaSC-TG2 from scratch. On the other hand, VGG19 has fewer parameters compared to ResNet50, because it is a lighter model with fewer layers (see Tables 5 and 6). VGG-19 also tends to be faster than ResNe-50 in terms of inference time. This is primarily due to its smaller number of parameters and the architectural design that facilitates efficient information flow. With fewer parameters to process and compute, VGG-19 requires less computational resources during inference, resulting in faster predictions. Therefore, when considering the number of parameters and inference time, VGG-19 has an advantage over ResNet50. It offers parameter efficiency and faster inference, which can be beneficial in scenarios where computational resources or real-time processing are important factors. Transfer learning leverages the knowledge gained from pre-training on a large-scale dataset and applies it to the target task. Since ResNet50 has a deeper architecture, its increased depth allows it to capture more complex and abstract features from the ImageNet dataset during its pre-training process. Therefore, when its knowledge is transferred to the remote-sensing domain, it performs better in the natural scene classification of remote-sensing images (See Table 4). ResNet50 employs skip connections that enable the model to propagate gradients more effectively during training, addressing the vanishing gradient problem. These skip connections help in the better flow of information and enable the model to learn more discriminative features. This allows the model to learn residual mappings, focusing on the differences between the input and output features. These residual connections help in the efficient propagation of gradients and enable the model to learn more easily. Here are some published results and their comparison with our model. (See Table 7). Hence, our state-of-the-art cross-domain transfer-learning-based model shows a superior performance when compared to these previously proposed methods for natural scene classification. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Training Mode** & **Trainable Parameters** & **Total Parameters** & **Inference Time** \\ \hline From scratch training & 20,029,514 & 20,029,514 & 3.08 s \\ \hline Last 3 layers fine-tuned & 5130 & 20,029,514 & 3.13 s \\ \hline Entire model fine-tuned & 20,029,514 & 20,029,514 & 3.23 s \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Training parameters and inference time for VGG-19.