# Aircraft Target Interpretation Based on SAR Images
## 2 Related Work
### Channel Attention Mechanism  
In order to enhance the feature expression ability of the convolutional neural network, some types of attention mechanisms have been proposed, such as SENet, CBAM [32], and SKNet [33]. The visual saliency of SAR images is different from that of natural images. Aircraft targets of SAR images show complex scattering spots and background noise. The network can more easily capture the feature region with significant contribution by integration attention mechanism. Different weights can automatically be assigned to each channel of the feature map by network learning, which can improve the expression ability of network features. SENet is the winner of the ImageNet2017 classification competition. The module is shown in Figure 2. It can learn the relationship between different channels, keep the attention of the network to channel information, evaluate the scores, and obtain weights of each channel adaptively.  
Figure 1: YOLOv5s network architecture.  
Where \(X\) is the input feature map, and \(U\in R\) (\(H\times W\times C\)) is obtained based on the transformation of \(X\), such as convolution, inception, or residual operation. \(U\) is squeezed along the spatial dimension by global average pooling operation to obtain \(Z\in R\) (\(1\times 1\times C\)). Each spatial dimension of the feature map is compressed into one pixel, which has a global receptive field of each spatial dimension. The output \(Z_{c}\) is expressed as follows:  
\[Z_{c}=F_{sq}(U_{c})=\frac{1}{H\times W}\overset{H}{\underset{i=1}{\sum}}\overset {W}{\underset{j=1}{\sum}}\overset{W}{\underset{1}{U_{c}}}(i,j) \tag{1}\]  
where \(U_{c}\) represents the \(c\)-th channel of the feature map; \(U_{c}(i,j)\) represents the pixel of the \(i\)-th row and \(j\)-th column of the \(U_{c}\); and \(F_{sq}\) represents the global average pooling operation. To reduce the complexity of the module, a fully connected (FC) operation is used to reduce the channel dimension to obtain \(R\) (\(1\times 1\times C\)/\(r\)). The length of the channel dimension is compressed by \(r\) times, then performs the ReLU activation function, and performs FC operation again to restore the channel dimension \(R\) (\(1\times 1\times C\)); finally, it is activated by the sigmoid function to generate the weights for each channel, The output \(S\) of the sigmoid function is expressed as follows:  
\[S=F_{ex}(Z,W)=\sigma[W_{2}\delta(W_{1}Z)] \tag{2}\]  
where \(F_{ex}\) is the feature excitation and reweight function; \(W_{1}\) and \(W_{2}\) are two FC operations, but the dimension of their input and output channel is different; \(\delta\) is the ReLU activation function; \(\sigma\) is the sigmoid activation function; and \(S\) is the weight matrix for each channel. The output \(\overset{\sim}{X_{c}}\) is expressed as follows:  
\[\overset{\sim}{X_{c}}=F_{scale}(U_{c},S_{c})=S_{c}\cdot U_{c} \tag{3}\]  
where \(S_{c}\) is the weight of \(c\)-th channel; and \(\overset{\sim}{X_{c}}\) is the final output with the weight information of each channel. After the integration of the SENet module, the extraction ability of useful features is enhanced.  
Figure 2: SENet module.