# Remote Sensing Scene Classification and Explanation Using RSSCNet and LIME
## 4 Results and Analysis
### Results and Analysis
#### 4.2.2 Experimental Results
The kappa coefficients of the RSSCN7 dataset were 0.9737 and 0.9329 at 50% and 20% training, respectively. Table 2 shows a comparison of the results obtained for the RSSCN7 dataset classification using different recently proposed methods, including the one proposed in this paper. The proposed two-stage cyclical learning-rate training method achieved the best overall accuracy for two different training ratios. With a 50% training ratio, it produced an increase in accuracy of about 3% compared with other methods. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **50\% Training Ratio** & **20\% Training Ratio** \\ \hline DBN [20] & 77.00 & NA \\ GoogLeNet [33] & 85.84 \(\pm\) 0.92 & 82.55 \(\pm\) 1.11 \\ CaffNet [33] & 88.25 \(\pm\) 0.62 & 85.57 \(\pm\) 0.95 \\ VGG-16 [33] & 87.18 \(\pm\) 0.94 & 83.98 \(\pm\) 0.87 \\ Deep Filter Banks [42] & 90.4 \(\pm\) 0.6 & NA \\ GCFs + LOFs [32] & 95.59 \(\pm\) 0.49 & 92.47 \(\pm\) 0.29 \\ RSSCNet (this paper) & 97.41 \(\pm\) 0.27 & 93.51 \(\pm\) 0.51 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of the overall accuracy and standard deviations using 50% and 20% training ratios on RSSCN7 dataset. Figure 16: Classification confusion matrix of our method on RSSCN7 dataset. The t-SNE analysis method was used to extract the deep features of the proposed model and to analyze it. In this section, the classification results obtained by applying this model to the WHU-RS19 dataset are discussed. As shown in Figure 17b, as a result of the training, the features became highly clustered, showing that the proposed model helps to improve the classification of the scene. Figure 18 shows the confusion matrix for the WHU-RS19 dataset extracted using VGG16 with the classification types proposed in this study and the optimal classification weights from the training early termination strategy. The training rate used was 60%. The matrix contains the individual classification results for the 19 categories in the dataset. Among these categories, the combinations football field and park and of forest and mountain were the most easily confused during the classification. Table 3 shows a comparison between the results of the WHU-RS19 dataset classification obtained using the proposed method and methods proposed in other recent papers. Using the two-stage cyclical learning-rate training method proposed in this study, our proposed method achieved the best overall accuracy. The kappa coefficients of the WHU-RS19 dataset were 0.9968 and 0.9874 at 60% and 40% training, respectively. From the confusion matrix classification results shown in Figure 18, it can be seen that the categories that are misclassified in the WHU-RS19 dataset include \"residential\", \"forest\", \"farmland\", and \"bridge\" (Figure 19a). We first used the LIME analysis on the misclassified four images and generated the super-pixel feature regions that the model was most interested in (Figure 19b). By observing the super-pixel area in Figure 19b, we can understand why the model misclassifies \"residential\" as \"industrial\", \"forest\" as \"park\", \"farmland\" as \"river\", and \"bridge\" as \"pond\". \begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **60\% Training Ratio** & **40\% Training Ratio** \\ \hline GoogLeNet [33] & 94.71 \(\pm\) 1.33 & 93.12 \(\pm\) 0.82 \\ CaffNet [33] & 96.24 \(\pm\) 0.56 & 95.11 \(\pm\) 1.20 \\ VGG-16 [33] & 96.05 \(\pm\) 0.91 & 95.44 \(\pm\) 0.60 \\ TEX-Net-LF [36] & 98.00 \(\pm\) 0.52 & 97.61 \(\pm\) 0.36 \\ Two-Stream Fusion [38] & 98.92 \(\pm\) 0.52 & 98.23 \(\pm\) 0.56 \\ RSSCNet (this paper) & 99.46 \(\pm\) 0.21 & 98.54 \(\pm\) 0.37 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of the overall accuracy and standard deviations using 60% and 40% training ratios on WHU-RS19 dataset. Figure 17: Visual analysis on WHU-RS19 dataset using t-SNE: (**a**) before training; (**b**) after training. Figure 19: Misclassified images on WHU-RS19 dataset: (**a**) original image; (**b**) super-pixel explanation by LIME analysis.