# Multifeature Fusion Neural Network for Oceanic Phenomena Detection in SAR Images
## 2 Multifeature Fusion Neural Network for Oceanic Phenomena Detection in SAR Images
### Multiscale Features Extraction  
We use an atrous spatial pyramid pooling module to extract multiscale features. This module is different from the traditional pyramid pooling module in that it uses atrous convolution to extract features. Atrous convolution is a convolution with zero weights in the kernel, which adds an atrous rate parameter to the traditional convolution. The atrous rate can be understood as the sampling step size between the convolution kernel elements, which determines the extent of the convolution kernel receptive field in the image. Because of the atrous rate, the atrous convolution has a greater receptive field than ordinary convolution of the same size. In general, the resolution of the result of the feature map obtained by different atrous convolution is the same.  
ASPP uses atrous convolution with different atrous rates arranged in parallel to achieve multiscale features extraction. Since the atrous rate of each channel is different, the receptive field and the scale information of the output feature maps also differ. Because the output feature maps of different channels have the same size, the feature maps can be directly connected together in the channel dimension, eliminating the problem of matching the size during upsampling. When the atrous rate is too large, the information of the output feature maps is almost entirely derived from the zero-padding area, and it will interfere with the result because these areas do not contain any useful information. In order to avoid this problem, we add a global average pooling channel.  
A square atrous convolution kernel in ASPP can perform well in extracting features from oceanic phenomena such as rain cells and oceanic eddies, but it will reduce the expression of the features of linear targets, such as ship wakes and oceanic fronts. Therefore, we add two special rectangular atrous convolution modules to ASPP to enhance the feature extraction of linear targets. The improved ASPP module is shown in Figure 3. This module improves the detection accuracy of ship wakes and oceanic fronts, and it reduces the discontinuity probability in linear target detection.  
To select the optimal atrous rate of ASPP under our sample dataset, we experimented with square atrous convolution and rectangular atrous convolution under different atrous rate combinations. When the atrous rate is set to {(2, 4, 6), 9\(\times\)1, 1\(\times\)9}, the detection accuracy is the highest. Therefore, we set the atrous rate of the three square atrous convolutions to 2, 4, and 6. A rectangular convolution kernel with a horizontal atrous rate X rate of 9 and a vertical atrous rate of 1 and another rectangular convolution kernel with a horizontal atrous rate X rate of 1 and a vertical atrous rate of 9 were set.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Layer Name** & **Network** & \begin{tabular}{c} **Output Size** \\ **(Channel \(\times\) Height \(\times\) Wide)** \\ \end{tabular} \\ \hline Conv1 & \begin{tabular}{c} \(\{3\times 3\) conv, stride 1, 64] \(\times 3\) \\ \(3\times 3\) max pool, stride2 \\ \end{tabular} & \(64\times\) h/2 \(\times\) w/2 \\ \hline Conv2 & \begin{tabular}{c} 1\(\times\)1 conv, stride 1, 64 \\ 3\(\times\)3 conv, stride 1, 64 \\ 1\(\times\)1 conv, stride 1, 256 \\ \end{tabular} & \(\times\)3 & \(256\times\) h/2 \(\times\) w/2 \\ \hline Conv3 & \begin{tabular}{c} 1\(\times\)1 conv, 128 \\ 1\(\times\)1 conv, stride 1, 512 \\ \end{tabular} & \(\times\)4 & \(512\times\) h/4 \(\times\) w/4 \\ \hline Conv4 & \
\begin{tabular}{c} 1\(\times\)1 conv, 256 \\ 3\(\times\)3 conv, stride 1, 256 \\ 1\(\times\)1 conv, stride 1, 1024 \\ \end{tabular} & \(\times\)6 & \(1024\times\) h/8 \(\times\) w/8 \\ \hline Conv5 &
\begin{tabular}{c} 1\(\times\)1 conv, 512 \\ 3\(\times\)3 conv, stride 1, 512 \\ 1\(\times\)1 conv, stride 1, 2048 \\ \end{tabular} & \(\times\)3 & \(2048\times\) h/16 \(\times\) w/16 \\ \hline \hline \end{tabular}
\end{table}
Table 1: ResNet-50 network architecture parameter settings.