# Remote Sensing Scene Classification and Explanation Using RSSCNet and LIME
## 4 Results and Analysis
### Results and Analysis
#### 4.2.3 Further Explanation and Discussion  
In this section, we further discuss how fine-tuning, a circular learning rate, and an increase in the amount of data can improve the classification performance. The classification results obtained in this study can be expanded to help understand the possible impact of this project on model training.  
1. The effectiveness of fine-tuning  
In Section 4 of this paper, two-stage training using the proposed model was described, and it was found that the proposed method has significant optimization for training. Moreover, we also wanted to understand, in addition to not carrying out freezing in the first stage, whether freezing the first 19 layers in the second stage would produce different results from those obtained by freezing different layers at two different stages. Therefore, we investigated three different situations: no freezing of any layer, freezing of the top seven layers, and freezing of top the 19 layers; the results for different combinations of these three situations are shown in Figure 22.  
The five combinations investigated were no freezing at either stage (shown as \"0 + 0\"), top seven layers frozen at second stage only (\"0 + 4\"), top 13 layers frozen at second stage only (\"0 + 13\"), top 13 layers frozen at first stage only (\"13 + 0\"), and top 13 layers frozen at two stages (\"13 + 13\"). From Figure 22, it can be seen that two-stage training with no freezing (\"0 + 0\") achieved the best testing accuracy. The test accuracy was the worst when the top 13 layers were frozen in the two training phases (\"13 + 13\"). According to the results of Figure 22, the test accuracy increases as the number of fine-tuning layers increases.  
Figure 21: Classification confusion matrix of WHU-RS19 dataset after image correction.  
2. Effectiveness of image data augmentation  
In this study, after inverting and increasing the number of images by carrying out a small amount of panning and zooming, augmentation training was also included in the training. As shown in Figure 23, doing this also successfully increased the training accuracy. The results here are shown as no data augmentation ((shown as \"1-stage\" in Figure 23), single-stage data augmentation included in the training (\"1-stage DA\"), and two-stage data augmentation included in the training (\"2-stage DA\").  
Figure 23: Accuracy of different training methods.  
Figure 22: Overall accuracy of fine-tuning using different frozen combinations.  
Effectiveness of using a two-stage cyclical learning-rate method  
In two-stage cyclical learning, the training can be implemented using two different optimizers. In this study, an SGD optimizer with a cyclical learning rate was used in the first stage. In the second stage, an Adam optimizer was used for the training. In two-stage cyclical learning-rate training, this method obtains the best weight in the first stage and then enter the second stage. When used together with the cyclical learning rate, this can greatly accelerate the convergence. Figure 24 shows a comparison of the number of iterations needed to achieve the best level of accuracy for three different training methods.  
When only a single-stage fixed learning rate (shown as \"1-stage\" in Figure 24) was used for the training, the convergence speed was the slowest and the lowest test accuracy was obtained. The use of a single-stage cyclical learning rate (\"1-stage CLR\") could speed up the convergence and give a greater chance of avoiding the local optimal solution so that better results could be obtained. When a two-stage circular learning rate (\"2-stage CLR\") was used in the early part of the second training stage, due to the change of optimizer, the process of finding the best accuracy fluctuated. However, overall, the best accuracy could be reached more quickly, using the smallest number of iterations of the three methods.