{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,8]],"date-time":"2026-07-08T05:14:35Z","timestamp":1783487675554,"version":"3.55.0"},"reference-count":35,"publisher":"MDPI AG","issue":"13","license":[{"start":{"date-parts":[[2026,6,26]],"date-time":"2026-06-26T00:00:00Z","timestamp":1782432000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"State Administration of Science, Technology and Industry for National Defence","award":["JCKY2024530C008"],"award-info":[{"award-number":["JCKY2024530C008"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Semantic segmentation technology based on Inverse Synthetic Aperture Radar (ISAR) images can provide crucial perception and analytical capabilities for intelligent safety maintenance of on-orbit spacecraft. However, conventional semantic segmentation methods suffer from three main limitations: firstly, the lack of modeling for radar physical characteristics in the \u201cimage first, segment later\u201d pipeline leads to loss of scattering information and phase details; secondly, reliance on extensive pixel-level manual annotation increases application costs; thirdly, ineffective utilization of spacecraft structural priors fails to guide networks to focus on the main body and edges of spacecraft segmentation. To address these issues, this paper proposes a complex-domain semantic segmentation framework named One-Stop Segmentation (OSS) based on ISAR echoes. The framework incorporates two innovative modules: an Automatic ISAR Labeling (AIL) method designed based on ISAR scattering characteristics to generate labels corresponding to ISAR echoes, and a complex-domain semantic segmentation network named One-Stop Segmentation Network (OSSNet) that performs semantic segmentation directly on echoes, avoiding information loss from imaging while shortening the data processing chain. Core contributions of OSSNet include: (1) a Domain Alignment Module (DAM) to effectively mitigate domain mismatch caused by data distribution differences between raw echo signals and labels; (2) a Multi-Perspective Attention (MPA) framework incorporating a Sliding Correlation Attention (SCA) module and a Subdomain Balanced Attention (SBA) module, lever-aging spacecraft structural priors to guide the network\u2019s focus on main structures and edge details from complementary perspectives, significantly improving segmentation ac-curacy. Experimental results on a simulated ground-based radar dataset demonstrate that the proposed OSS framework achieves a mean Intersection over Union (mIoU) of 92.13% and a mean F1-score of 95.75% in ISAR spacecraft semantic segmentation tasks, outperforming existing methods.<\/jats:p>","DOI":"10.3390\/s26134075","type":"journal-article","created":{"date-parts":[[2026,6,29]],"date-time":"2026-06-29T05:49:33Z","timestamp":1782712173000},"page":"4075","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Complex-Domain Semantic Segmentation of Spacecraft Directly from ISAR Echoes"],"prefix":"10.3390","volume":"26","author":[{"given":"Aoxiang","family":"Pan","sequence":"first","affiliation":[{"name":"Space Engineering University, Beijing 101400, China"},{"name":"National Key Lab of Space Target Awareness, Beijing 101400, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yonghua","family":"He","sequence":"additional","affiliation":[{"name":"Space Engineering University, Beijing 101400, China"},{"name":"National Key Lab of Space Target Awareness, Beijing 101400, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yonggang","family":"Li","sequence":"additional","affiliation":[{"name":"Space Engineering University, Beijing 101400, China"},{"name":"National Key Lab of Space Target Awareness, Beijing 101400, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiahao","family":"Wang","sequence":"additional","affiliation":[{"name":"Space Engineering University, Beijing 101400, China"},{"name":"National Key Lab of Space Target Awareness, Beijing 101400, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ruitao","family":"Shen","sequence":"additional","affiliation":[{"name":"Space Engineering University, Beijing 101400, China"},{"name":"National Key Lab of Space Target Awareness, Beijing 101400, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Weigang","family":"Zhu","sequence":"additional","affiliation":[{"name":"Space Engineering University, Beijing 101400, China"},{"name":"National Key Lab of Space Target Awareness, Beijing 101400, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,6,26]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"252","DOI":"10.1016\/j.cja.2020.05.016","article-title":"Imaging of target with complicated motion using ISAR system based on IPHAF-TVA","volume":"34","author":"Cherif","year":"2021","journal-title":"Chin. 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