{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,5]],"date-time":"2025-11-05T06:53:45Z","timestamp":1762325625389,"version":"build-2065373602"},"reference-count":48,"publisher":"MDPI AG","issue":"13","license":[{"start":{"date-parts":[[2023,6,27]],"date-time":"2023-06-27T00:00:00Z","timestamp":1687824000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62102296","XJS222221","XJS222215"],"award-info":[{"award-number":["62102296","XJS222221","XJS222215"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["62102296","XJS222221","XJS222215"],"award-info":[{"award-number":["62102296","XJS222221","XJS222215"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Target detection and segmentation in synthetic aperture radar (SAR) images are vital steps for many remote sensing applications. In the era of data-driven deep learning, this task is extremely challenging due to the limited labeled data. Few-shot learning has the ability to learn quickly from a few samples with supervised information. Inspired by this, a few-shot learning framework named MSG-FN is proposed to solve the segmentation of ship targets in heterologous SAR images with few annotated samples. The proposed MSG-FN adopts a dual-branch network consisting of a support branch and a query branch. The support branch is used to extract features with an encoder, and the query branch uses a U-shaped encoder\u2013decoder structure to segment the target in the query image. The encoder of each branch is composed of well-designed residual blocks combined with filter response normalization to capture robust and domain-independent features. A multi-scale similarity guidance module is proposed to improve the scale adaptability of detection by applying hand-on-hand guidance of support features to query features of various scales. In addition, a SAR dataset named SARShip-4i is built to evaluate the proposed MSG-FN, and the experimental results show that the proposed method achieves superior segmentation results compared with the state-of-the-art.<\/jats:p>","DOI":"10.3390\/rs15133304","type":"journal-article","created":{"date-parts":[[2023,6,28]],"date-time":"2023-06-28T00:45:11Z","timestamp":1687913111000},"page":"3304","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Multi-Scale Similarity Guidance Few-Shot Network for Ship Segmentation in SAR Images"],"prefix":"10.3390","volume":"15","author":[{"given":"Ruimin","family":"Li","sequence":"first","affiliation":[{"name":"Academy of Advanced Interdisciplinary Research, Xidian University, Xi\u2019an 710071, China"}]},{"given":"Jichao","family":"Li","sequence":"additional","affiliation":[{"name":"Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, Xidian University, Xi\u2019an 710071, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2619-6481","authenticated-orcid":false,"given":"Shuiping","family":"Gou","sequence":"additional","affiliation":[{"name":"Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, Xidian University, Xi\u2019an 710071, China"}]},{"given":"Haofan","family":"Lu","sequence":"additional","affiliation":[{"name":"Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, Xidian University, Xi\u2019an 710071, China"}]},{"given":"Shasha","family":"Mao","sequence":"additional","affiliation":[{"name":"Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, Xidian University, Xi\u2019an 710071, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7586-4020","authenticated-orcid":false,"given":"Zhang","family":"Guo","sequence":"additional","affiliation":[{"name":"Academy of Advanced Interdisciplinary Research, Xidian University, Xi\u2019an 710071, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,6,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"751","DOI":"10.1109\/LGRS.2018.2882551","article-title":"Squeeze and excitation rank faster R-CNN for ship detection in SAR images","volume":"16","author":"Lin","year":"2019","journal-title":"IEEE Geosci. 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