# Aerial Scene Classification through Fine-Tuning with Adaptive Learning Rates and Label Smoothing
## 3 Results
### Classification of the Nwpu-Resisc45 Data Set  
The experimental results of our proposed method with linear and RBF SVM for the NWPU-RESISC45 dataset are displayed in Tables 4 and 5 and Figure 13. Table 4 shows the achieved classification accuracy for a 20%/80% train/test split ratio of the data set. It can be noticed that for linear decay scheduler and as well for CLRs, the linear SVM classifier gives better overall accuracy compared to softmax classifier for all pre-trained CNN. Table 5 shows the obtained classification accuracy for a 10%/90% train/test split ratio for the NWPU-RESISC45 data set. Both train/test split ratios for the analyzed datasets are chosen in order to make experimental comparisons with other studies in the corresponding field of research, which use the same proportions of train/test splits. Here the achieved experimental results are similar to the ones from Table 4. The linear SVM classifier outperforms the softmax classifier in all cases except when we fine-tune the InceptionV3 neural network and Xception with a linear decay scheduler.  
better than ours. However, they use multiple fusion of features extracted from dataset images or their parts with different dimensions (scale). Instead, we utilized fine-tuning with one image size according to the pre-trained CNNs requirements.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Method** & \multicolumn{2}{c}{**20\% Training Ratio**} & **10\% Training Ratio** \\ \hline GoogleNet [46] & 78.48 & 76.19 \\ VGG-16 [46] & 79.79 & 76.47 \\ AlexNet [46] & 79.85 & 76.69 \\ Two-stream fusion [15] & 83.16 & 80.22 \\ BoCF [46] & 84.32 & 82.65 \\ Fine-tuning AlexNet [46] & 85.16 & 81.22 \\ Fine-tuning GoogleNet [12] & 86.02 & 82.57 \\ SAL-TS-Net (Yu et Liu 2018a) & 87.01 & 85.02 \\ VGG-16+MSCP [51] & 88.93 & 85.33 \\ Fine-tuning VGG-16 [46] & 90.36 & 87.15 \\ D-CNNs with VGGNet-16 [12] & 91.89 & 89.22 \\ Triplet networks [59] & 92.33 & / \\ Inception-V3-CapsNet [58] & 92.60 & 89.03 \\ DenseNet121 with linear decay scheduler and linear SVM classifier (proposed) & 93.37 & 90.46 \\ DenseNet121 with linear decay scheduler and RBF SVM classifier (proposed) & 93.60 & 90.42 \\ EfficientNet-B3-aux [4] & 93.81 & 91.08 \\ Dense-based CNNs + 3D pooling [58] & 94.95 & 92.9 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Overall accuracy (%) of the proposed method compared to reference methods with 20% and 10% of NWPU-RESISC45 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 20% training ratio.  
\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 & 89.42 & 89.74 & 90.03 \\ Cyclical learning rate & 89.20 & 89.70 & 89.99 \\ \hline
**InceptionV3** & & & \\ \hline Linear decay scheduler & 90.16 & 90.07 & 90.36 \\ Cyclical learning rate & 90.21 & 90.18 & 90.36 \\ \hline
**Xception** & & & \\ \hline Linear decay scheduler & 89.62 & 89.59 & 89.18 \\ Cyclical learning rate & 89.40 & 89.65 & 89.67 \\ \hline
**DenseNet121** & & & \\ \hline Linear decay scheduler & 90.25 & 90.46 & 90.42 \\ Cyclical learning rate & 89.73 & 89.99 & 90.11 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Overall accuracy (%) of the proposed method with a 10%/90% train/test ratio of NWPU-RESISC45 data set. The bold text highlights the best accuracy per classifier.