## 2 Materials and Methods
### Feature Selection  
Traditional target structure interpretation typically categorizes complex target scattering mechanisms into canonical structures such as sphere, plate, trihedral corner, dipole, and dihedral corner reflectors, as illustrated in Figure 6a. All of these are strongly differentiated in terms of their scattering properties. The clustered scatter plot presented in Figure 6b provides visual evidence of this phenomenon. The scattering mechanism of the components is the coupling of the aforementioned canonical structure scattering mechanisms. Therefore, the separability of the components is reduced. Additionally, the three polarization decomposition methods discussed earlier collectively yield thirteen polarization features, which poses a risk of dimensionality collapse. To enhance the representational capability of the feature vectors and reduce the training complexity of the classification network, this study employs the electromagnetic computation data of the aforementioned canonical structures as the training samples for feature selection. Several feature selection methods are employed to ensure that the optimal feature subset is chosen, including ReliefF, Least Absolute Shrinkage and Selection Operator (LASSO), Random Forest Feature Importance (RFFI), Principal Component Analysis (PCA), and Maximum Relevance Minimum Redundancy (MRMR). The resulting feature vectors and their corresponding classification performance results are presented in Table 1 and Figure 7 below. SVM classifiers were used to obtain these results. The Radial Basis Function (RBF) was selected as the kernel function type, and its kernel scale was 0.61. The  
Figure 6: (**a**) Canonical structures and (**b**) cluster scattering plots of canonical structures. regularization parameter C was 1. The validation method used to examine the predictive accuracy of the SVM classifier was five-fold cross-validation. According to Table 1 and Figure 7, it can be seen that the feature vector obtained by the ReliefF algorithm achieved the best classification performance. The results show that the feature vector selected by ReliefF achieves better TPR among all candidates, indicating its strong ability to correctly identify positive samples. In addition, the corresponding FPR is lower than alternative feature sets. These results validate the ability of the optimization strategy to effectively suppress false alarms while maintaining sensitivity. Based on these results, the ReliefF algorithm was chosen to analyze the polarization features of canonical structures' electromagnetic simulation data. The ReliefF algorithm is an efficient method for feature selection when solving multiclass classification problems [42]. It can evaluate the relevance of both features and classes. Assuming that there exists a training set \(D\), the algorithm randomly selects a sample \(R\) from \(D\), searches for the nearest-neighbor sample \(H\) from the samples of the same class as \(R\), and finds the nearest-neighbor sample \(M\) from the samples of a different class than \(R\). The algorithm then updates the weights of each feature according to the following rules. If the distance between \(R\) and \(H\) on a feature is smaller than the distance between \(R\) and \(M\), then this feature is useful for distinguishing the nearest neighbors between the same class and different classes and the algorithm increases the weight of this feature. Conversely, if the distance between \(R\) and \(H\) on a feature is greater than the distance between \(R\) and \(M\), then the weight of this feature is decreased. The above process is repeated \(m\) times, finally obtaining the average weights of each feature. We selected features based on their weight values to construct a feature vector \([|\tau_{i}-\tau_{s}|,|\tau_{i}+\tau_{s}|,|v|,\gamma,K_{e},K_{h}]\). The terms \(|\tau_{i}-\tau_{s}|\) and \(|\tau_{i}+\tau_{s}|\) characterize the symmetry and reciprocity of a target, respectively.