# Intelligent Detection and Segmentation of Space-Borne SAR Radio Frequency Interference
## 5 Experimental Results
### Results of the Interference Segmentation Experiment  
In this section, measured Sentinel-1 quick-look images of the PAIS dataset are used for validation and comparison. The RFI artifacts are annotated for ground truth. By conducting the aforementioned designs, the semantic segmentation capacity of SISNet is advanced. Then, the proposed SISNet is trained. To test the semantic segmentation performance of the proposed SISNet, we select some existing semantic segmentation networks such as FCN, Mask RCNN, UNet++, and U2 Net, and train these networks with the same parameters; the results obtained are used as comparative test results. The training is carried out on an HP workstation in the school laboratory. The GPU used is a GeForce RTX 3060 12 GB, the CPU is an Intel i7-10700, and the computer has 64 GB of RAM. Due to the limitations of hardware conditions, the U2 Net network parameters are too large, leaving us no other options but to run the U2 NetP network, which is the lightweight version of U2 Net, on the terminal. Therefore, the SISNet trained on the workstation is also formed based on U2 NetP and the improvements mentioned above. Data augmentation automatically expands the training set by providing modified data from the original data. To estimate the quality of the segmentation, we use the five-fold cross-validation technique (i.e., training and validating five times) and present the averaged results. Thus, the dataset is randomly divided into a training subset and validation subset at a ratio of 4:1 in each training stage. To quantitatively prove the superiority of SISNet, we conduct hypothesis testing on the differences among indicators from test samples. All these experiments are performed in a similar pattern. For fair comparison, all state-of-the-art models are set with default, official, original parameters, such as the number of neurons and the number of network layers. In addition, the initial learning rate of training is 0.05 and descends in a polynomial form, and the model iterates for 50 epochs. During training, each epoch is iterated and becomes valid once, and the final result is the mIoU, accuracy, precision, and F1 score of the epoch when the mIoU is the highest. The average mIoU of the proposed network and the networks used in the comparison test are calculated after the five-fold cross-validation training, as shown in Figure 16(b) below, and the change in the average loss is shown in Figure 16(a). It can be concluded that the mIoU curve of SISNet fluctuates less, indicating the model can fit the training data well and has a stronger learning ability for unbalanced datasets. In addition, the mIoU curve of SISNet completely exceeds the mIoU curve of UNet and U2 Net after the 30 th epoch, showing that it can achieve better segmentation performance than U2 Net. At last, the results show that after the 40 th epoch, the error bars of SISNet's mIoU and F1 curves do not overlap with those of other models, indicating that our method significantly improves segmentation performance. In addition, when training SISNet, the five-fold cross-validation experiments are performed, and we present the mIoU results of five subtests with the average mIoU results in Figure 17(a). The corresponding training loss of subtests and the average loss are show in Figure 17(b). The combined average mIoU results of SISNet compared to different networks are added with the standard deviation in the form of vertical lines in individual epochs (see Figure 19). The common sense is that when the difference between the means of two samples is greater than two standard deviation, the change has significance. It can be further concluded from the figure that SISNet significantly improves RFI segmentation performance compared to other semantic segmentation networks.