# Intelligent Detection and Segmentation of Space-Borne SAR Radio Frequency Interference
## 4 Methodology
### SISNet Structure  
In the medical field, since the ultimate goal of medical imaging is to assist doctors in clinical diagnosis, it is far from enough for the network to tell doctors whether a patient is sick from a 3D CT. The doctor still wants to know which layer and where the lesion is located. Similarly, in the field of RFI detection, knowing whether there is interference in the SAR image is only the first step in the cognition of RFI. We also need to know the location of the RFI: that is, which burst the RFI is in on the Sentinel-1 satellite SAR image. Therefore, this paper also aims to use an image segmentation method on the quick-look images of Sentinel-1 SAR data to realize the positioning of RFI. Here we propose an improved U2 Net called SISNet. SISNet replaces the pooling layer and loss function used by the U2 Net network to adapt to the pixel-level segmentation  
Figure 10: Ghost module: replacing convolution operation with linear transformation. task for RFI. Then, a modified receptive field module is introduced to replace part of the dilated convolution layer of the U2 Net bottom layers to effectively increase the receptive field and the feature extraction ability of the network. In addition, a lightweight CBAM module is added at the connection of each layer of the encoder-decoder to focus on the key information in the feature map. Moreover, before outputting the predicted mask results, we also introduce a channel attention module to assign weights among the predicted masks output by each layer. Finally, the two-dimensional convolution in the RSU module is replaced by a residual convolution to prevent network degradation. In 2019, [PERSON] [54] found that CNN is not translation invariant because when the input is slightly translated or transformed, the output changes dramatically. [PERSON] believed that this is caused by operations with down-sampling (such as convolution with \(stride>2\) and pooling), as these down-sampling methods ignore common sense in the field of signal processing: before the signal is down-sampled, low-pass filtering is required for anti-aliasing. However, directly inserting the low-pass filter into the network incurs performance degradation. Previously, in order to increase the number of training samples, we used transformations such as translation and flipping, which may cause the CNN-like encoder to obtain completely different feature maps in the deep layer when faced with similar input samples, making the network unable to learn effective discriminant features. Therefore, [PERSON] combined a low-pass filter with the existing method and proposed the BlurPool method. We can write max-pooling as a composition of two functions:  
\[\text{MaxPool}_{k,s}=\text{Subsample}_{s}\circ\text{Max}_{k}\,, \tag{7}\]  
where \(\circ\) stands for hadamard product, which means multiplying the corresponding positions of the matrix. The Max operation, as it is densely evaluated in a sliding window fashion, preserves shift-equivariance, but subsequent sub-sampling does not. [PERSON] proposed to add an anti-aliasing filter known as \(Blur_{m}\) with kernel \(m\times m\), as shown in Figure 11.  
Then, blurring and sub-sampling operations are combined, as is commonplace in image processing and is called \(BlurPool_{m,s}\). \[\text{MaxPool}_{k,s} \rightarrow \text{Subsample}_{s}\circ\text{Blur}_{m}\circ\text{Max}_{k} \tag{8}\] \[= \text{BlurPool}_{m,s}\circ\text{Max}_{k}\,,\]  
However, it is worth mentioning that BlurPool does not solve the loss of translation invariance entirely, but it alleviates it to a large extent. As was discussed before, RFI shows multi-scale characteristics, which affects the balance of the samples. To tackle this problem, the focal loss function is used in SISNet. Focal Loss [55] is a variation of binary cross-entropy loss and introduces two new hyper-parameters: \(\alpha_{t}\) and \(\gamma\).