Semi-Supervised Structural Prior-Guided Network for Space Target Component Segmentation in ISAR Images

Abstract

Highlights What are the main findings? The GMHC-ViT encoder broadens feature representation via multi-stream gating, effectively mitigating inter-class confusion, while the PGM injects shape and edge structural priors into weak-response channels, markedly improving boundary accuracy and component completeness. SSPNet consistently achieves superior segmentation performance across different annotation ratios and exhibits strong robustness under extremely limited labels and severe noise conditions. What are the implications of the main findings? The proposed SSPNet enables highly accurate and label-efficient space target component segmentation, greatly reducing the reliance on expensive pixel-level annotations in ISAR interpretation tasks. The work demonstrates the benefit of explicitly encoding domain-specific structural knowledge into deep networks, offering a valuable reference for related tasks. Abstract Segmenting key components of space targets using Inverse Synthetic Aperture Radar (ISAR) images is an important interpretation task in space situational awareness. However, the scarcity of pixel-level annotated data, inter-class confusion caused by morphological differences among multiple target classes, and the absence of structural priors for components restrict the performance improvement in existing deep models on this task. Therefore, this paper proposes a Semi-Supervised Structural Prior-Guided Network (SSPNet). First, a Gated Manifold-Constrained Hyper-Connections Vision Transformer (GMHC-ViT) encoder is proposed to broaden the feature representation space via parallel multi-feature streams with adaptive gating, thereby alleviating inter-class confusion and enhancing cross-category generalization. Second, a Prior-Guided Module (PGM) is proposed to extract shape and edge priors of components, and it adaptively enhances the weakly activated channels of encoder features through cross-attention, thereby injecting structural knowledge independent of image quality into the segmentation process. Furthermore, to effectively leverage large amounts of unlabeled data, a strong perturbation strategy tailored to the characteristics of ISAR images is designed for consistency regularization. Experimental results on a simulated ISAR dataset containing 38 classes of space targets demonstrate that SSPNet outperforms existing methods and exhibits strong segmentation capability even under low signal-to-noise ratio (SNR) conditions.