## 1 Introduction
One task of our feature extractor is to utilize the unique electromagnetic information contained in SAR data to improve the performance of our CIL method on SAR targets, while balancing the stability of SAR electromagnetic information with the plasticity of CNN features. The second task of the feature extractor is to provide clustering space for unseen classes. The classifier is required to achieve fair classification when training without the former class data. In summary, this work makes three main contributions:  
1. To make full use of the information contained in SAR echo data and to generalize a more balanced feature representation in terms of stability and plasticity, a feature fusion network, called SCF-Net, is proposed. First of all, the VGG16 [30] network is modified as a CNN feature extractor to extract the image feature. Meanwhile, we apply the bag of visual words (BOVW) model--originally proposed in [31; 32] and recently applied to scattering feature transformation in [33]--to convert the electromagnetic attributed scattering center feature (SC feature) into a feature vector. Inspired by the effects of the attention mechanism on information processing, we utilize the cross-attention architecture to fuse the SC feature and CNN feature, and by integrating the stable inherent electromagnetic characteristics with plastic CNN feature, a more balanced representation in terms of stability and plasticity is generated. 2. Analyzing the feature clustering in feature space, this paper concludes that an appropriate degree of overfitting could improve the performance of CIL in our specific model setting. Researchers always try to avoid overfitting in network training to improve the performance. However, for our feature that is fused with the stable SC feature, slight overfitting can make the features of the current classes to cluster more tightly in feature space, thereby improving classification performance when new classes are added, albeit at the cost of a slight decrease in recognition accuracy. According to this conclusion, two measures are applied, including removing the dropout structure and applying the \"late-stop\" strategy. By improving the feature clustering degree, our training strategy can reduce the probability that features of  
Figure 2: Overview of the SCF-CIL framework, including a cross-attention feature fusion module (SCF-Net), an overfitting training strategy for the feature extractor, and a multi-stage regularization method for the classifier. The feature extractor produces SC features and CNN features, which are fused through a cross-attention mechanism and further refined by multiple fully connected layers, serving as the input to the final classifier. former classes fall into the new class subspace when trained with newly arrived data. This is another improvement in our optimization of the balance between plasticity and stability. 3. For the classifier in the network, a multi-stage regularization method is proposed to correct the recognition bias caused by catastrophic forgetting. Through analyzing the operation of the classifier, the regularization is applied to three parts involved in the classifying calculation, including the magnitude of the feature vector, the magnitude of the classifier weight vector, and the angle between them. By a staged regularization operation, the impact of catastrophic forgetting is transferred to the vector magnitude that is easier to correct. In this part, an angle constraining loss (AC loss) is introduced to constrain the angle change between new instances and old class classifier weights. The rest of this article is organized as follows. Section 2 introduces the related work about CIL and SAR data feature fusion. The proposed SCF-CIL method is described in Section 3. Section 3 first introduces our SCF-Net, including the CNN feature extractor, SC feature extractor, and the cross-attention structure. Then, the impact of overfitting on feature space distribution is analyzed.