# Intelligent Detection and Segmentation of Space-Borne SAR Radio Frequency Interference
## 4 Methodology
### SISNet Structure
The focal loss function form is as follows:  
\[\text{FocalLoss}(p_{t})=-\alpha_{t}(1-p_{t})^{\gamma}\log(p_{t}), \tag{9}\]  
Figure 11: Max-Blur-Pooling: an anti-aliasing max-pooling method implemented by applying low-pass filters before down-sampling. where the addition of \(\alpha_{t}\) enables the loss function to balance the relative importance of positive and negative samples, increase the weight of negative sample loss, and reduce the weight of positive sample loss so that the model tends to focus on the learning of negative sample rules: that is, under this circumstance, this is expressed as focusing on the pixel samples occupied by RFI artifacts; \(p_{t}\) is the predicted category probability, whose value range is \([0,1];1-p_{t}\) is called the modulation factor. When a sample is misclassified, \(p_{t}\) is very small. At the same time, the modulation factor is close to 1, which has no effect on the original loss. When \(p_{t}\) approaches 1, the modulation factor is close to 0. Under this condition, the losses of easy examples are down-weighted. The variable \(\gamma\) controls the rate at which time the weight is dropped. When \(\gamma=0\), focal loss is the same as the cross entropy loss function. In the original paper, it is pointed out that the best experimental effect is when \(\gamma=2\)[55]. Inspired by the receptive field structure in the human visual system, [PERSON] et al. [56] proposed a novel module, receptive field block (RFB), which is a combination of an inception structure and atrous spatial pyramid pooling (ASPP) module, that strengthens the deep features learned from a lightweight CNN model, making the detection model faster and more precise. Specifically, RFB utilizes multi-branch pooling with different convolution kernels corresponding to RFs of different sizes, applies dilated convolutional layers to control their eccentricity, and reintegrates them to generate the final output. The author built RFBNet based on the RFB module. Thanks to this module, RFBNet reached quite good results. Due to the few restrictions imposed on the network architecture, RFB can be embedded in most networks as a general module. Therefore, we introduced a modified version of RFB called RFB-d into the bottom layers of U2 Net for multi-scale feature extraction. The RFB-d module has the similar structure of RFB, but the dilated convolution rate of its dilated convolutional layer is modified to a larger value to adapt to the bottom layers of U2 Net in order to achieve the purpose of obtaining a larger receptive field. The architecture of RFB-d is shown in Figure 12.  
Different from other computer vision applications, RFI artifacts are highly correlated with the polarimetric property, which leads to absence in certain channels in the image. This unique property facilitates the attention mechanism due to obvious contrast between RGB channels. It is shown that the RFI artifacts are not distributed equally in RGB channels and exist in limited locations in the image. For this reason, we add the LCBAM module  
Figure 12: The architecture of the modified receptive field block with larger dilation rates. proposed above to the intermediate connection between the encoder and the decoder to realize the weight distribution of channels and spatial features. Furthermore, the U2 Net model refers to the idea of FPN, which performs two-dimensional up-sampling on each output of the decoder stage and fuses these temporary outputs together to perform a 2D convolution to output the final result. Before being output as the final result, these temporary outputs are presented as multi-channel prediction masks until a 2D convolution is through. Since the underlying prediction masks are obtained through up-sampling multiple times, there is a certain loss of accuracy in these prediction masks, which is unfavorable for the final output.