# Remote Sensing Scene Classification and Explanation Using RSSCNet and LIME
## 4 Results and Analysis
### Results and Analysis  
#### 4.2.1 Analysis of Experimental Parameters  
In this study, different pre-trained models were tested for the evaluation of the classification results. This was done so that the best feature extraction method for the appropriate pre-trained model could be found. Once it was found, the weight layers in the pre-trained model were adjusted. It was considered whether the pre-trained model weights would affect the results of the transfer learning in order to find the best training-layer training plan for the model parameter configuration that would be used in subsequent experiments in this study. 1. Different pre-trained CNN models  
Different pre-trained models have different degrees of influence on image feature extraction. In this experiment, the WHU-RS19 dataset was used to embed four different common pre-trained models--VGG16 [21], VGG19 [21], ResNet50 [30], and InceptionV3 [31]--into the classification model. A classification performance test was carried out to help decide which of the four models was the most suitable for use as the pre-trained model. The training used an Adam optimizer; the batch size was 64, and the number of iterations was 150. The results of the training carried out using the four different models are shown for comparison in Figure 8. The results show that the pre-trained VGG16 model had the best overall accuracy; thus, in subsequent testing, this was used as the image feature extraction model. Figure 8: Comparison of accuracy using different pre-trained models. Different numbers of fine-tuning layers during training  
After choosing to use the VGG16 pre-trained model, this study further explored whether, by freezing some of the network layers in the pre-trained model, the model could be made to have a better generalization performance. This experiment was also conducted using the WHU-RS19 dataset, and the results are shown in Figure 9. Based on the four blocks contained in the VGG16 model, two, four, seven, 10, and 13 layers were frozen. The results show that, when no layers were frozen, all the layers of the pre-trained model were retrained and fine-tuned, which means that, although this method requires more resources and a longer training time, it can produce a better overall accuracy. This shows that, in the classification of remote sensing images, the feature image that is required can be obtained by further training. For the different fine-tuning layers discussed, the main consideration was whether to retrain the weights in the pre-trained model. Retraining the entire network inevitably takes a lot of time. However, in the process of feature extraction, in addition to training, we believe that it is necessary to focus on the training of the classifier and, hence, in this study, we aimed to strengthen the model's ability to classify features by using a two-stage training method. In Figure 10, the WHU-RS19 dataset is used as the comparison dataset for the proposed method. A two-stage training method (shown as \"2-stage\" in Figure 10) in our research indicates that two different optimizers were used and separated into two parts for the two-stage training. In the first stage, the SGD (stochastic gradient decent) optimizer was used to carry out 100 training iterations to train the entire neural network model. In this stage, the pre-trained model and classifier model could be adjusted at the same time, thus strengthening the features of the capture model and learning the classification ability. In the second stage of the training, the model weights with the best accuracy learned in the first stage were loaded, and the Adam optimizer was used to carry out the next 50 training iterations. We also compared the result with only using the Adam optimizer training for 150 iterations (shown as \"1-stage\" in Figure 10). As can be seen from Figure 10, the test accuracy obtained using the two-stage training (97.76%) was better than that obtained using the one-stage training (96.33%). Figure 9: Comparison of accuracy using different fine-tuning layers. 3.