# Aerial Scene Classification through Fine-Tuning with Adaptive Learning Rates and Label Smoothing
## 3 Results  
### Classification of Aid Dataset  
The experimental results of the proposed method for classification of the AID dataset with SVM classifiers are shown in Tables 1 and 2, for 50%/50% and 20%/80% train/test split ratio, respectively. The above mentioned ratios are common in the literature and they are used in our experiments in order to compare the achieved accuracy with other authors' research. As can be seen from Table 1, when we use ResNet50 and DenseNet121, which architecture is based on shortcut connections, the linear SVM classifier yields better classification accuracy when compared to a softmax classifier. However, when it comes to the inception based pre-trained CNNs InceptionV3 and Xception, the situation is the opposite, and the classification results are better with the softmax classifier when compared to classification with linear SVM of the extracted features from fine-tuned networks. Analysis of Table 2, which depicts the experimental results for 20%/80% train/test split ratio, shows slightly different outcomes: the softmax classifier works better for InceptionV3, Xception, and DenseNet121, but classification with linear SVM classifier is a better option for ResNet50. One possible explanation for this phenomenon is that SVM performs better with vector data of lower dimensionality. On the contrary, higher dimensionality has less impact on softmax classification. In fact, inspecting the neural network architectures mentioned above, we can notice that the ResNet50 architecture presents a fully connected layer of size 512, whereas Inception-based and DenseNet201 architectures present a fully connected layer of 1024 and 1920 units, respectively. Comparing the softmax and the RBF SVM classification of the AID dataset shows that the RBF SVM classifier outperforms the softmax classifier for 50%/50% train/test split ratio in all simulation scenarios, except for the InceptionV3 and Xception neural network architectures with linear decay scheduler. For the 20%/80% train/test split ratio of the AID dataset, RBF SVM achieves better classification accuracy than softmax, except for ResNet50, InceptionV3, and DenseNet121 with linear decay scheduler. Table 3 presents a comparison of the proposed method to other state-of-the-art techniques. We achieved the best classification results on the AID dataset with a 50% training set for DenseNet121 with a linear decay scheduler and a RBF SVM classifier, and with a 20% training set for Xception with a linear decay scheduler and a RBF SVM classifier. To the best of our knowledge, our proposed method for 50% training set of AID dataset outperforms all of the other methods in the literature. The standard deviation of achieved classification accuracy of AID dataset is in interval \(\pm\) (0.1-0.4). Figures 9 and 10 show the confusion matrices for the AID dataset with a 50%/50% train/test split ratio for ResNet50, linear learning rate decay, and softmax or linear SVM classifier, respectively. Because the \begin{table}
\begin{tabular}{l c c c} \hline \hline
**Method** & **Softmax Classifier** & **Linear SVM Classifier** & **RBF SVM Classifier** \\ \hline
**ResNet50** & & & \\ \hline Linear decay scheduler & 95.62 & 95.88 & 96.12 \\ Cyclical learning rate & 95.52 & 95.83 & 96.08 \\ \hline
**InceptionV3** & & & \\ \hline Linear decay scheduler & 96.41 & 96.32 & 95.96 \\ Cyclical learning rate & 95.95 & 95.82 & 96.18 \\ \hline
**Xception** & & & \\ \hline Linear decay scheduler & 96.14 & 96.04 & 95.96 \\ Cyclical learning rate & 96.15 & 95.97 & 96.30 \\ \hline
**DenseNet121** & & & \\ \hline Linear decay scheduler & 96.03 & 96.10 & 98.03 \\ Cyclical learning rate & 96.21 & 96.3 & 96.60 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Overall accuracy (%) of the proposed method with a 50%/50% train/test ratio of the AID dataset. The bold text highlights the best accuracy per classifier.