# Research on Scene Classification Method of High-Resolution Remote Sensing Images Based on RFPNet
## 3 Proposed Method
### The Structure of RFPNet
#### 3.2.1 The Residual Block  
With the development of deep learning technology, CNN has made a breakthrough in the field of image classification. However, as the depth of the convolutional neural network model increases, gradient disappearance or gradient explosion will occur and even cause degradation if the depth  
Figure 5: Schematic diagram of the RFPNet structure. Where, Conv is the abbreviation of convolutional layer, Max_pool is the abbreviation of max-pooling layer, and FC layer is the abbreviation of fully connected layer.  
is continuously deepened. In this context, ResNet was proposed in 2015 and has been used widely. Existing network modules of ResNet series include Resnet-18, Resnet-34, Resnet-50, Resnet-101, Resnet-152, and so on.  
ResNet consists of several residual blocks which can greatly improve network performance. The main characteristic of the residual block is to introduce shortcut connections, which are those parts skipping one or more layers, thus making the learning goal of the network become the minimization of residuals. Assuming that the input of the multi-layer network is \(x\) and the expected mapping output is \(H(x)\), the optimization objective changes from fitting output \(H(x)\) to fitting the difference between output and input \(H(x)\)-\(x\). The plain network and the residual block are shown in Figure 6. The plain network is to learn a complete output, while the residual block is to learn the difference between the output and input.  
In the residual block, what the residual mapping learned can be expressed as follows:  
\[F=W_{2}\sigma(W_{1}x), \tag{12}\]  
where \(\sigma\) represents the nonlinear function ReLU, and the biases are omitted for the sake of convenience. The output expression of the residual block is determined by Equation (13).  
\[H(x)=F(x,\{W_{i}\})+x, \tag{13}\]  
Although shortcut connections are used, Equation (13) does not introduce new parameters and increase the calculation amount. It should be noted that the \(x\) and \(F(x,\{W_{i}\})\) in Equation (13) must have the same dimension. When the input and output dimensions need to be changed, such as changing the number of channels, the linear transformation of \(x\) can be made in the shortcut connection and then connected to the following layer, the expression is as follows:  
\[H(x)=F(x,\{W_{i}\})+W_{s}x, \tag{14}\]  
The partial derivative Equation (13) is as follows:  
\[\frac{\partial H}{\partial x}=\frac{\partial x+\partial F(x,W)}{\partial x}=1+ \frac{\partial F(x,W)}{\partial x}, \tag{15}\]  
It can be seen from Equation (15) that the residual block is highly sensitive to small changes.  
There are two kinds of typical structures of the residual block named 2-layer residual block and 3-layer residual block, as shown in Figure 7. In the 2-layer residual block, the input of the k\(th\) layer is connected with the output of k+2\(th\) layer by shortcut connection, and their vector addition result is taken as the input of k+3\(th\) layer. Since only vectors with the same dimension can be added,  
Figure 6: The contrastive image of the plain network and the residual block: (**a**) the plain network; (**b**) the residual block.  
the dimensions of feature maps are reduced and increased in the 3-layer residual block by two \(1\times 1\) convolutional kernels.