## 5 Discussion
### Experiments on Scattering Center Feature  
The first group of experiments aims to evaluate two improvements of our SCF-Net, including fusing scattering center feature and the cross-attention mechanism. In order to compare the impact of fusing the scattering center feature on algorithm performance, we first compare the CIL performance when using only CNN feature, only SC feature, and our fused feature separately on the MSTAR dataset under SOCs. For the baseline using only the CNN feature, we adopt the VGG 16 network. For our proposed fused feature, our SCF-Net is applied. For the evaluation of only using SC feature, we simply replace the cross-attention structure in our SCF-Net with a fully connected layer to process the SC feature, while keeping the rest of the network architecture unchanged. The distillation loss is applied in the training of only using CNN feature and only using SC feature. In addition, because VGG16 is an end-to-end training network, it can be optimized using the distillation loss, which means that it follows the LWF method and can represent the performance of the LWF on our dataset. The comparison is shown in Figure 8.  
Figure 8: Experiment results for different features. Figure 8 shows the CIL results of (1) blue: only using the image feature extracted by CNN (CNN feature), (2) green: only using the SC feature, and (3) yellow: fusing the image feature and scattering center feature (SC feature). In the experiment on the CIL with only SC feature, the accuracy on training set can reach 97.28%, which means the SC feature contains enough information for classification task. However, the accuracy on validation dataset (different depression angle) is only 88.07%, which means training with only SC feature under the current network structure leads to limited generalization capability. On the other hand, while the SC feature may not achieve optimal recognition performance at the basic training stage, it exhibits a slower decline in accuracy during subsequent CIL stages compared to using only CNN feature, indicating its robustness in maintaining consistent target representations. Compared with CNN feature, it is significant that our fusing feature not only greatly improves the accuracy in the CIL process but also achieves a better classification result at the basic learning stage. In other words, using the fused features can not only effectively improve the performance of the CIL process but also achieve state-of-the-art performance in conventional classification tasks. As discussed in Section 4.2, the impact of catastrophic accumulates over every incremental learning step. Based on the accumulated catastrophic forgetting, the method using only CNN features shows an earlier degradation than our method. The most significant disparity between the two methods in the CIL process occurs in stage 4, with our classification accuracy reaching 84.749%, while the method using VGG16 decreases to 47.282%. This is mainly because it lacks the stability and strong generalization ability provided by the SC features. By combining SC features, the fused features become more robust, making the model better able to handle the effects of accumulated forgetting across incremental stages. The previous experiment validates the effectiveness of fusing the SC feature. Typically, when fusing multiple feature vectors, researchers often employ fully connected layer operations. To demonstrate the superiority of our cross-attention feature fusion method, we conducted comparative experiments, as illustrated in Figure 9. The blue line (Fully Connected) represents the CIL results obtained by applying the fully connected layer method for feature fusion, while the orange line (Attention) represents our cross-attention method. In Figure 9, the disparity in CIL performance between the two methods is significantly smaller compared to the previous set of comparative experiments, providing further evidence that fused scattering center features can improve CIL performance.