## 4 Results
### Experimental Results  
As shown in Table 2, we conducted a comparative experiment on various currently popular CIL methods, including iCaRL [19], ER [52], EWC [13], LWF [45], the Joint training method, and the None method. The Joint Training method is equivalent to training a network on all known classes, so it can be seen as the upper target of CIL. In contrast, the None method directly trains the data of the current categories in each CIL stage without taking any measures to solve catastrophic forgetting. In the new CIL stage, the None method only expands the classifier output dimension and trains the network using only the new instances. As a result, the None method at each stage can maintain only the classification ability of the current context, while the classification accuracies of former classes drop to nearly 0.2. The None method can be seen as the lower target of CIL. In addition to the above-mentioned methods, iCaRL and ER are replay methods, while EWC and LWF are regularization-based methods. Following the hyperparameters settings in some open-source implementations, the EWC regularization coefficient \(\lambda\) is set to \(10^{6}\). The sample budget is set to 20 samples per old class in both ER and iCaRL. For the methods used for comparison, they are directly applied to the MSTAR dataset without any modifications. Therefore, the above methods directly extract features from the MSTAR target images for training. The five stages of the CIL experiment are conducted on these methods under the SOCs of the MSTAR dataset as used in our method. In each stage, the average accuracy \(\overline{acc}\) of all seen classes (former classes and new classes in the current stage) is reported for each algorithm in Table 2.  
Since the basic learning stage does not involve the problem of catastrophic forgetting, the classification performance of these methods is not significantly different. In this stage, our method fuses richer features, leading to a slight increase in classification performance. In stage 2, the LWF method achieves the best performance, second only to joint training. However, the accuracy of LWF drops rapidly in the subsequent incremental learning stages. Its accuracy in stage 5 is only 41.645%, which is only better than the lower target. In contrast, iCaRL and ER achieve relatively stable and high performance during the incremental process. This is mainly due to the fact that these two methods have access to a part of former instances that can be used to train the network. For our method, because it does not have access to former instances, it is inevitable that its classification performance will decrease as CIL progresses. However, through our contribution, our method effectively improves the classification performance in the face of catastrophic forgetting. As shown in Table 2, our method achieves the best performance in the first 4 stages without considering \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Basic Learning** & **Stage 2** & **Stage 3** & **Stage 4** & **Stage 5** \\ \hline _None_ & _98.082_ & _52.893_ & _43.469_ & _34.420_ & _30.861_ \\ _Joint training_ & _98.082_ & _96.377_ & _95.761_ & _97.829_ & _96.284_ \\ ICaRL [19] & 97.995 & 79.536 & 63.801 & 65.272 & 60.000 \\ ER [52] & 98.082 & 76.634 & 71.932 & 77.997 & 63.719 \\ LWF [45] & 98.082 & **96.252** & 64.933 & 47.282 & 41.645 \\ EWC [13] & 98.082 & 80.043 & 71.970 & 67.321 & 46.267 \\
**Ours** & **98.540** & 95.984 & **90.495** & **84.749** & **67.044** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of experiment results of different CIL methods on the MSTAR dataset (average accuracy %). Italic font indicates the upper and lower bounds for reference, and bold font highlights the best CIL performance in each CIL stage. the upper target. Compared with LWF, which also has no access to old instances, our method has a significantly smaller decrease in accuracy at each CIL stage.