# Remote Sensing Scene Classification and Explanation Using RSSCNet and LIME
## 4 Results and Analysis
### Results and Analysis
#### 4.2.2 Experimental Results  
1. Classification of UC Merced land-use dataset  
In this section, the classification results for the UC Merced land-use dataset are discussed. The t-SNE analysis method was used for the classification. This method is a non-linear dimensionality-reduction algorithm used for exploring high-dimensional data. It can map multi-dimensional data to two or more dimensions using technology suitable for visual presentation. In this study, extracting the features of the deep layers for analysis helped with the understanding of the differences between the features obtained by the model before and after training. In Figure 12(a), which shows the results before training, the features extracted by the model show little correlation; however, after training, as shown in Figure 12(b), the features are highly clustered, thus showing that these models do help to improve the classification performance. Figure 14 shows the VGG16 model supplemented by the model classification matrix proposed in this study using the best classification weights obtained from the early training termination strategy. The resulting confusion matrix is also shown; the training rate was 80%. The matrix contains the individual classification results for 21 categories. The kappa coefficients of the UC Merced dataset were 0.9985 and 0.9895 at 80% and 50% training, respectively. In Table 1, the classification results found in this study are compared with those obtained using other classification methods. It can be seen that, by using the two-stage cyclical learning-rate training method, this study achieved the best overall accuracy out of the results shown. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **80\% Training Ratio** & **50\% Training Ratio** \\ \hline GoogLeNet [33] & 94.31 \(\pm\) 0.89 & 92.70 \(\pm\) 0.60 \\ CaffNet [33] & 95.02 \(\pm\) 0.81 & 93.98 \(\pm\) 0.67 \\ VGG-16 [33] & 95.21 \(\pm\) 1.20 & 94.14 \(\pm\) 0.69 \\ salM\({}^{3}\)LBP-CLM [34] & 95.75 \(\pm\) 0.80 & 94.21 \(\pm\) 0.75 \\ CNN-R+VLAD with SVM [35] & 95.85 & NA \\ TEX-Net-LF [36] & 96.62 \(\pm\) 0.49 & 95.89 \(\pm\) 0.37 \\ VGG19+Hybrid-KCRC [37] & 96.33 & NA \\ Two-Stream Fusion [38] & 98.02 \(\pm\) 1.03 & 96.97 \(\pm\) 0.75 \\ CTFCNN [39] & 98.44 \(\pm\) 0.58 & NA \\ GCFs + LOFs [32] & 99.00 \(\pm\) 0.35 & 97.37 \(\pm\) 0.44 \\ Inception-v3-CapsNet [40] & 99.05 \(\pm\) 0.24 & 97.59 \(\pm\) 0.16 \\ MVFLN+VGG-VD16 [41] & 99.52 \(\pm\) 0.17 & NA \\ RSSCNet (this paper) & 99.81 \(\pm\) 0.06 & 98.76 \(\pm\) 0.19 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of the overall accuracy and standard deviations using 80% and 50% training ratios on UC Merced dataset. Figure 13: Visual analysis on UC Merced dataset using t-distributed stochastic neighbor embedding (t-SNE): (**a**) before training; (**b**) after training. 2. Classification of 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 RSSCN7 dataset are discussed. As shown in Figure 15b, after training, the features became highly clustered, which shows that the model proposed by this research helps to improve the scene classification. Figure 16 shows the overall accuracy confusion matrix extracted by VGG16 supplemented by the classification method proposed in this study and using the optimal classification weights in the  
Figure 14: Classification confusion matrix of our method on UC Merced dataset. Figure 15: Visual analysis on RSSCN7 dataset using t-SNE: (**a**) before training; (**b**) after training. training early termination strategy. The training rate used was 50%. The matrix contains the individual classification results for seven categories. Among these, agricultural land and grassland are most likely to be confused. This is perhaps because the two categories have similar characteristics--both containing a large proportion of green ground, which easily leads to errors.