# Intelligent Detection and Segmentation of Space-Borne SAR Radio Frequency Interference
## 4 Methodology
### Interference-Targeted Quick-Detection Network  
An important step in recognizing and mitigating radio frequency interference is to determine the existence of interference in the SAR data. To judge for the existence of RFI, we can intuitively observe from the SAR image. If there are bright lines or block artifacts in the image, it can be considered that there are RFI artifacts in the data. However, SAR satellites can generate a large amount of SAR data every moment, and it is time-consuming and labor-intensive to screen SAR data through manual discrimination. Therefore, we attempted to realize intelligent discrimination by training a neural network to replace the manual work. Using the classic convolutional neural network model to classify and recognize images has problems such as a large number of parameters, a large amount of calculations, a large model, and high equipment requirements. Previously, [PERSON] fan et al. [24] realized RFI detection based on time-frequency domain images by applying a VGG network. However, the number of VGG network parameters is as high as 138 million, which severely limits its inference speed. In the face of massive SAR data, the speed of screening interference data is particularly important. The MobileNet V2 [25] network is a lightweight, deep convolutional neural network proposed by Google for intelligent embedded devices. The core of the network is the combination of depth-wise separable convolution and an inverted residual block, which achieves better performance with a smaller amount of computation. But it still meets the problem of low recognition accuracy and large prediction errors. Aiming to solve these problems, a modified MobileNet V2 called 'IQDN' is proposed, the framework for which is shown in Figure 9. Firstly, by integrating the spatial and channel attention mechanism [51] (convolutional block attention module (CBAM)), the network model can enhance the refinement ability of the interference features and reduce the impact of irrelevant features. Secondly, we replace the part of convolution module with a ghost module, reducing the number of parameters while ensuring accuracy, and finally, we replace the ReLU6 activation function in the depth separable convolution module with LeakyReLU to retain more positive and negative feature information in the image. The idea of the attention mechanism comes from the human visual attention process, and attention focus is added to the key area to obtain the required details. CBAM [51] is an attention module that includes a channel attention module and a spatial attention module so that the model can simultaneously carry on channel calibration and spatial calibration. However, the fully connected layer and convolution kernel with a large receptive field will greatly increase the parameter number of the module. In the improved model proposed in this paper, by introducing a lightweight channel and spatial attention module (LCBAM), the design uses a one-dimensional convolution operation to aggregate channel features for one-dimensional channel attention and uses dilated convolution with a kernel size of \(3\times 3\) in two-dimensional spatial attention to aggregate spatial features, which minimizes the  
Figure 9: Architecture of proposed IQDN. number of module parameters while better obtaining interference features. Considering that the deep layer of the convolutional neural network extracts features such as shape and semantics, we propose a modified MobileNet V2 wherein the LCBAM module is added between the last inverted residual module and the average pooling layer in order to improve the feature extraction ability.