## 1 Introduction
To solve this problem, GEM [20] realized incremental learning by modifying the updati  
Figure 1: The overall illustration of the class-incremental learning (CIL) process, where new classes (red arrow) are introduced stage by stage, and the model incrementally learns to classify all seen classes. the current model. PackNet [21] iterated the process of training, pruning, and retraining to incrementally learning tasks. However, PackNet needs to manually adjust the pruning rate for different tasks, and due to limited network parameters, its task capacity is also limited. HAT [22] utilized a hard attention mask to keep part of the parameters unchanged and used the rest to learn new tasks. HAT is robust to different hyperparameter choices and has no limit on the number of total tasks. Typical CIL networks can generally be divided into a feature extractor and a classifier, as shown in Figure 1. In the CIL process, the two structures collaborate to learn new knowledge while maintaining old knowledge. Specifically, the extractor should be able to extract the feature with enough completeness to distinguish classes beyond the known classes. Meanwhile, the classifier needs to maintain the ability to classify the former classes when trained with the data from current classes. When the CIL method tries to preserve the previous knowledge (i.e., stability), it often does so at the expense of reducing the method's ability to learn new knowledge (i.e., plasticity) [23]. Based on the distinct roles of these two structures in the classification algorithm, this paper explores achieving a balance between plasticity and stability in the feature extractor and classifier, respectively. In the above methods, most of them focus on optical image datasets [24]. However, SAR datasets exhibit substantial disparities compared to optical datasets, which causes a performance decrease when directly applying commonly used CIL methods to SAR datasets. The SAR image of the same target is significantly different as the depression angle changes, placing higher requirements on data feature extraction in the CIL process. In addition, SAR data have narrower frequency bandwidths, whereas optical imagery spans multiple spectral bands, rendering optical images rich in information, unlike SAR images which are comparatively less informative. Last but not least, complex SAR echo data contain lots of target information, such as target component information. Directly converting SAR echo data to visualized SAR images will result in the loss of a large amount of target information, which also makes it difficult for the CIL performance to reach the expected level. [PERSON] et al. [25] mentioned that a robust and well-generalized representation of the training sample could increase the performance of the CIL algorithm, while SAR data contains rich target information that can be utilized. Many efforts have been made to extract and fuse SAR target features for classification. [PERSON] et al. [26] proposed a new size-related feature that is helpful for classifying tactical ground targets in a high-resolution SAR image. [PERSON] et al. [27] utilized the discrete-defined Krawtchouk moments to represent the military targets, which can realize robust classification with few features. [PERSON] et al. [28] proposed a stacked autoencoder (SAE) to fuse 23 baseline features and a three-patch local binary pattern feature, which made the fused features more distinguishable and realizes better classification performance. [PERSON] et al. [29] fused the global and local features for SAR automatic target recognition under extended operation conditions. Inspired by the above methods, this paper aims to explore a feature fusion method to effectively utilize the electromagnetic information, improving the CIL performance on SAR targets. Based on the above discussion, our SCF-CIL method is proposed as shown in Figure 2. There are three main tasks for our method, two for the extractor and one for the classifier.