# Research on Scene Classification Method of High-Resolution Remote Sensing Images Based on RFPNet
## 2 Related Work
### Convolutional Neural Network  
The convolutional neural network (CNN) was proposed by [PERSON] [25] in 1980 and revised by [PERSON] [26]. In recent years, CNN has shown outstanding performance in natural language processing [27], disaster discovery [28], synthetic biomedicine [29], optical fiber communication [30], holographic image reconstruction [31], artificial intelligence program of go [32] and so on. With the advent of the era of big data, it is possible to train complex models with large-scale datasets on high-performance computing platforms (such as high-performance computers, graphics workstations, cloud computing platforms, etc.). In this context, many convolution neural network models have been proposed, such as LeNet [33], AlexNet [34], Overfeat model [35], VGGNet [36], GoogleNet [37], SPPNet [38], ResNet [39], MobileNets [40], ZFNet [41], DenseNet [42], and so on. CNN is a multi-layer network structure, whose basic structure mainly includes input layer, convolutional layer, pooling layer, fully connected layer, and output layer, as shown in Figure 1. The following are introduced in details. 1. Input layer: The input layer is the input of the whole CNN. In the neural network of image processing, it generally represents the pixel matrix of the image. 2. Convolutional layer: The convolutional layer is used to extract image features. Low-level convolutional layer extracts shallow features (such as edges, lines, and corners). High-level convolutional layer further learns abstract features through the input of low-level features. The convolutional layer obtains multiple feature activation maps by convolving the convolution kernel of a specific size with the previous layer, as shown in Equation (1). \[X_{j}^{l}=f\Bigg{[}\sum_{i\in M_{j}}{(X_{i}^{l-1}*K_{ij}^{l}+b_{j}^{l})}\Bigg{]},\] (1) where \(M_{j}\) represents the input image set, \(X_{j}^{l}\) represents the \(j\)th feature map of the \(l\) layer, \"*\" represents the operation of convolution, \(X_{i}^{l-1}\) is the \(i\)th feature map of the \(l\)-\(l\) layer, \(K_{ij}^{l}\) represents the filter connecting the \(j\)th feature map of the \(l\) layer and the \(i\)th feature map of the \(l\)-\(l\) layer, \(b_{j}^{l}\) is the bias, \(f[\cdot]\) represents the nonlinear activation function that can solve problems that cannot be solved by linear models. The activation functions commonly used include sigmoid, tanh, ReLU, etc. The calculation equation is as follows: \[\text{sigmoid}:f(x)=\frac{1}{1+e^{-z}},\] (2) \[\text{tanh}:f(x)=\frac{e^{z}-e^{-z}}{e^{z}+e^{-z}},\] (3) \[\text{ReLU}:f(x)=\max(0,x),\] (4)
3. Pooling layer: The introduction of a pooling layer is to reduce dimension and abstract the input image by imitating the human visual system. By sampling the convolved feature maps, the useful information of the image is preserved and the redundant data is removed, thus effectively preventing the overfitting problem and speeding up the computation speed. What's more, the pooling layer has feature invariance which can make the model more concerned with the presence of certain features rather than the specific location of the features and tolerate some small displacement of features. There are generally two kinds of operations: maximum pooling and average pooling. The calculation equation of pooling layer is as follows: \[X_{j}^{l}=f\Big{[}g_{j}^{l}down(X_{i}^{l-1})+b_{j}^{l}\Big{]},\] (5) where \(down(\cdot)\) is a sub-sampling function, \(\beta\) represents the sub-sampling coefficient, \(X_{j}^{l}\) represents the \(j\)th feature map in the \(l\) layer, \(X_{i}^{l-1}\) is the \(i\)th feature map in the \(l\)-\(l\) layer, \(b_{j}^{l}\) is the bias, and \(f[\cdot]\) represents the nonlinear activation function. 4. Fully connected layer: The fully connected layer is usually used in the last layers of the network, which can combine the information transmitted in the former layers to achieve the explicit expression of classification. Figure 1: Basic structure of CNN. Based on the first idea, [PERSON] et al.