## 1 Introduction  
As an increasing number of nations develop the capability to launch extensive satellite constellations, space target surveillance has emerged as a critical method for countries to compete for advantageous positions in outer space. ISAR is an essential element of space target observation systems. It is distinguished by its capability to operate in all weather conditions and its strong wide-area monitoring attributes [1]. Identifying the structure of a space target is important for space target surveillance. Some target components have a distinct correlation to the type of targets, and can be used to evaluate the function of non-cooperative space targets. Research on space targets has attained significant achievements in ISAR imaging and processing. High-resolution imaging is important for space target analysis. An advanced scheme based on blind source separation to separate and suppress clutter from the range ambiguity region was put forward by [2]. ISAR image semantic segmentation dividesthe space target into non-overlapping regions, and can be viewed as a method of recognizing component structures [3; 4; 5; 6; 7]. On the basis of a deep feature aggregation network (DFANet), [8] fused features of various resolutions and scales, with the result outperforming other segmentation algorithms. In addition, ISAR images contain 3D information of component structures. ISAR three-dimensional geometry reconstruction technology overcomes the lack of height-direction information and visualizes the 3D components structures of targets [9; 10; 11; 12]. What is more, it has the potential to identify component structures in ISAR images based on advanced scattering center models and parameter extraction [13; 14; 15]. In [16], the sparsity of ISAR images was utilized to convert radar images into a set of scatter points by employing the Random Sample Consensus (RANSAC) algorithm for scattering point matching. [PERSON] et al. [17] applied semantic segmentation to the extraction of scatter centers. However, most of these methods primarily utilize the amplitude information of ISAR images, and it is difficult to effectively identify small components that occupy a small number of pixel units. To solve this problem, it is necessary to expand the dimensionality of information and use deep learning methods to obtain deeper feature extraction. Computer vision has been extensively utilized for target recognition and classification in recent years. Convolutional Neural Networks (CNNs) are commonly employed in remote sensing tasks such as target detection and image recognition, leading to the development of models such as YOLOv8 [18], PT-CCNN [19], P-DCNN [20], IITR-Net [21], and SDRnet [22]. In [23; 24], the authors discussed the joint processing of ISAR images and optical images, while in [25; 26; 27] the focus was on the fusion of multiple ISAR images. Additionally, [28] presented a target recognition technique for ISAR images based on polar coordinates and shape matrix descriptions. These methods have significantly advanced classification and recognition techniques for space targets. In addition, advanced deep learning models in remote sensing, such as the Mampa model [29; 30], Memory-Augmented Auto-Encoder (MAAE) with adaptive reconstruction [31], and two-stream isolation forest [32] have the potential to be applied to space target structure identification. As an intrinsic property of electromagnetic waves, polarization is highly sensitive to the physical attributes of a target, including its dielectric constant, geometrical shape, and space orientation [33]. [PERSON] et al. optimized the phase gradient autofocus algorithm by utilizing the Cloude-Pottier decomposition method parameters \(H\) and \(\alpha\), resulting in significantly elevated image quality [34]. In [35; 36], the authors employed polarization correlation patterns for the interpretation of ISAR images.