ISAR Images Segmentation Based on Spatially Variant Mixture Multiscale Autoregressive Model

Ju, Yanwei; Zhang, Yan; Guo, Feng

doi:10.1109/iaeac.2018.8577781

Cited by 1 publication

(1 citation statement)

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“…Therefore, the segmentation performance is not satisfactory when the relevant segmentation methods of optical images are used to carry out the semantic segmentation study of ISAR images [ 13 ]. At the same time, as a relatively cutting-edge research topic in RATR, the ISAR image segmentation methods are rarely researched, and the recognition effect is unsatisfactory [ 14 , 15 ].…”

Section: Introductionmentioning

confidence: 99%

An ISAR Image Component Recognition Method Based on Semantic Segmentation and Mask Matching

Zhu,

Zhang,

et al. 2023

Sensors

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The inverse synthetic aperture radar (ISAR) image is a kind of target feature data acquired by radar for moving targets, which can reflect the shape, structure, and motion information of the target, and has attracted a great deal of attention from the radar automatic target recognition (RATR) community. The identification of ISAR image components in radar satellite identification missions has not been carried out in related research, and the relevant segmentation methods of optical images applied to the research of semantic segmentation of ISAR images do not achieve ideal segmentation results. To address this problem, this paper proposes an ISAR image part recognition method based on semantic segmentation and mask matching. Furthermore, a reliable automatic ISAR image component labeling method is designed, and the satellite target component labeling ISAR image samples are obtained accurately and efficiently, and the satellite target component labeling ISAR image data set is obtained. On this basis, an ISAR image component recognition method based on semantic segmentation and mask matching is proposed in this paper. U-Net and Siamese Network are designed to complete the ISAR image binary semantic segmentation and binary mask matching, respectively. The component label of the ISAR image is predicted by the mask matching results. Experiments based on satellite component labeling ISAR image datasets confirm that the proposed method is feasible and effective, and it has greater comparative advantages compared to other classical semantic segmentation networks.

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Section: Introductionmentioning

confidence: 99%