# Remote Sensing Scene Classification and Explanation Using RSSCNet and LIME
## 3 Method
### Convolutional Neural Network Model  
To start, the VGG [21] neural network trained by ImageNet was used as the model for image feature extraction. This method adjusts the structure of the neural network used for certain trained network models by using transfer learning to perform other image training tasks. In the experiments carried out in this study, the structure of the original neural network layer was adjusted by freezing the weights in one or more layers of the original model, minus the time to retrain the deep model. The original model was used for feature extraction, and the newly embedded model layer was trained for use in classification. It was, therefore, only necessary to update and modify the weights of the newly added network layer during training; the frozen layer weights that were transferred from the learning model could be kept, as shown in Figure 4[22].  
After removing the top-level fully connected layers of the pre-training model, a new classification network was added. In our model, we chose an exponential linear unit (ELU) [23] as our activation function because it can reduce the vanishing gradient problem by identifying positive values. The ELU  
Figure 4: Convolutional neural network model.  
Figure 3: Example images of WHU-RS19 dataset: (**a**) airport; (**b**) beach; (**c**) bridge; (**d**) commercial; (**e**) desert; (**f**) farmland; (**g**) football field; (**h**) forest; (**i**) industrial; (**j**) meadow; (**k**) mountain; (**l**) park; (**m**) parking; (**n**) pond; (**o**) port; (**p**) railway station; (**q**) residential; (**r**) river; (**s**) viaduct.  
has negative values; hence, it allows the mean unit to approach zero for a deep neural network model and obtain a faster convergence than the rectified linear unit (ReLU). Regularization was also added as a tool to reduce overfitting in the network. Regularization can be considered as a penalty term in the loss function. The so-called \"penalty\" refers to restrictions that are applied to some parameters in the loss function to prevent overfitting. Moreover, we added a batch normalization layer [24] after the convolution layer in our model, which can act as a regularizer to decrease overfitting. Batch normalization can use a larger learning rate in model training to achieve faster convergence benefits.  
Considering the balance between the network capacity and the test accuracy and discussing the influence of different regularization and optimization strategies, we finally designed this new deep learning network architecture with a high generalization capability. The proposed CNN architecture is called RSSCNet (Figure 5). In RSSCNet, the depth of the weight layers is 17, and the mathematical formulation is written as follows:  
\[\left\{\begin{array}{c}X=f^{(16)}\Big{(}f^{(15)}\Big{(}\cdots\Big{(}f^{(2)} \Big{(}f^{(1)}(\mathbf{x},\ \mathbf{w},\ \mathbf{b})\Big{)}\Big{)}\Big{)}\Big{)}\\ Y=Softmax(X)\end{array}\right\}, \tag{1}\]  
where \(\mathbf{x}\) is the input data of each image, \(\mathbf{w}\) is the weight matrix, \(\mathbf{b}\) is the bias, \(X\) is the representative feature of \(\mathbf{x}\), and \(Y\) is the output probability. The RSSCNet architecture includes 15 convolutional layers, five max pooling layers, one global average pooling layer, two batch normalization layers [24], one dropout layer, and two fully connected layers with a softmax classification. Note that the activation function of the last two convolutional layers uses an ELU [23], while the other weight layers use the ReLU activation function. The convolution filter size is \(3\times 3\). The dropout rate is 0.5. The regulations of L1 and L2 are 0.01 and 0.02, respectively.  
Figure 5: CNN classifier network architecture of our RSSCNet model.