# Aerial Scene Classification through Fine-Tuning with Adaptive Learning Rates and Label Smoothing
## 3 Results
### Classification of Aid Dataset
\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 & 93.06 & 93.09 & 92.98 \\ Cyclical learning rate & 92.91 & 93.47 & 93.44 \\ \hline
**InceptionV3** & & & \\ \hline Linear decay scheduler & 93.7 & 93.32 & 93.50 \\ Cyclical learning rate & 93.79 & 93.41 & 93.93 \\ \hline
**Xception** & & & \\ \hline Linear decay scheduler & 93.67 & 93.29 & 94.14 \\ Cyclical learning rate & 93.44 & 93.36 & 93.65 \\ \hline
**DenseNet121** & & & \\ \hline Linear decay scheduler & 93.74 & 93.26 & 93.56 \\ Cyclical learning rate & 93.54 & 93.35 & 93.58 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Overall accuracy (%) of the proposed method with a 20%/80% train/test ratio of the AID dataset. The bold text highlights the best accuracy per classifier. classification accuracy achieved with softmax and linear SVM is close, both confusion matrices differ only in the classification outcome for a small number of images. Fine-tuning of Xception with 20% of the AID dataset as a training set for CLR or linear decay learning rate scheduler and the softmax classifier is depicted in Figures 11 and 12, respectively. Two plots show only the fine-tuning of all network layers with the SGD optimizer, but not the warming-up of network head. From the plots, we can see that training with CLR is more stable with characteristic picks on training and validation loss curves, causing some oscillations that are visible as a waved shape. Additionally, it is noticeable that the training loss is more prominent than validation loss on both Figures, because we have applied label smoothing on training labels only. \begin{table}
\begin{tabular}{l c c} \hline \hline
**Method** & **50\% Training Ratio** & **20\% Training Ratio** \\ \hline GoogleNet+SVM [45] & 86.39 & 83.44 \\ VGG-VD-16 [45] & 89.64 & 86.59 \\ CaffeNet [45] & 89.53 & 86.86 \\ salM\({}^{3}\)LBP-CLM [49] & 89.76 & 86.92 \\ MCNNs [50] & 91.80 & / \\ Fusion by addition [51] & 91.87 & / \\ X-Net-LF [52] & 92.96 & 90.87 \\ ARCNet-VGG16 [14] & 93.10 & 88.75 \\ VGG-16 (fine-tuning) [53] & 93.60 & 89.49 \\ VGG-16+MSCP [54] & 94.42 & 91.5 \\ Two-stream fusion [22] & 94.58 & 92.32 \\ Multilevel fusion [55] & 95.36 & / \\ GBNet + global Feature [53] & 95.48 & 92.20 \\ Xception with linear decay scheduler and RBF & 95.96 & 94.14 \\ SVM classifier (proposed) & & \\ InceptionV3-CapsNet [56] & 96.32 & 93.79 \\ EfficientNet-B3-aux [4] & 96.56 & 94.19 \\ GCFs + LOFs [57] & 96.85 & 92.48 \\ D-CNNs with VGGNet-16 [12] & 96.89 & 90.82 \\ Dense-based CNNs + 3D pooling [58] & 97.19 & 95.37 \\ DenseNet121 with linear decay scheduler and & 98.03 & 93.56 \\ RBF SVM classifier (proposed) & & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Overall accuracy (%) of the proposed method compared to reference methods with 50% and 20% of the AID data set as a training set. For our method, we selected the best results obtained for the two training ratios, and report them in bold. Methods are ordered in ascending order by their performance on the 50% training ratio. Figure 10: Confusion matrix of the proposed method with a 50%/50% train/test ratio of AID data set for ResNet50, linear learning rate decay, and a linear SVM classifier. Figure 9: Confusion matrix of the proposed method with a 50%/50% train/test ratio of AID data set for ResNet50, linear learning rate decay, and softmax classifier. Figure 11: Training plot of the proposed method with 20% of AID dataset as the training set for Xception, cyclical learning rate, and softmax classifier. Figure 12: Training plot of the proposed method with 20% of AID data set as the training set for Xception, linear learning rate decay, and softmax classifier.