Scattering Characteristics Guided Network for ISAR Space Target Component Segmentation
Fengjun Zhong, Fei Gao, Tianjin Liu, Jun Wang, Jinping Sun, Huiyu Zhou
Abstract
Affected by the large dynamic range of gray values, strong scattering point edge effect, noise and clutter, inverse synthetic aperture radar (ISAR) images have problems such as boundary blurring and target discontinuity, which bring great challenges to ISAR space target component segmentation. In this paper, a novel ISAR space target component segmentation method, called scattering characteristics guided network (SCGN), is proposed. First, a cross-scale self-attention module (CSSAM) is proposed, which establishes global relationships in different dimensions during cross-scale feature fusion, refining the detailed features of the target while suppressing high sidelobe scattering points and noise. Second, a novel component scattering center extractor (CSCE) is proposed to combine scattering center distribution with the network via explicit supervision. Finally, a novel scattering characteristics-assisted segmentation head (SCASH) is proposed, which introduces the scattering characteristics of each component into the mask segmentation process and models the semantic interdependencies over long distances through a spatial attention mechanism to achieve fine-grained component segmentation. Experimental results on the ISAR simulation dataset and realistic ISAR images show that SCGN outperforms existing methods.