{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:16:01Z","timestamp":1760145361516,"version":"build-2065373602"},"reference-count":43,"publisher":"MDPI AG","issue":"14","license":[{"start":{"date-parts":[[2024,7,17]],"date-time":"2024-07-17T00:00:00Z","timestamp":1721174400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the Natural Science Foundation of China","award":["62172321"],"award-info":[{"award-number":["62172321"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The consistent speckle noise in SAR images easily interferes with the semantic information of the target. Additionally, the limited quantity of supervisory information available in one-shot learning leads to poor performance. To address the aforementioned issues, we creatively propose an SAR target recognition model based on one-shot learning. This model incorporates a background noise removal technique to eliminate the interference caused by consistent speckle noise in the image. Then, a global and local complementary strategy is employed to utilize the data\u2019s inherent a priori information as a supplement to the supervisory information. The experimental results show that our approach achieves a recognition performance of 70.867% under the three-way one-shot condition, which attains a minimum improvement of 7.467% compared to five state-of-the-art one-shot learning methods. The ablation studies demonstrate the efficacy of each design introduced in our model.<\/jats:p>","DOI":"10.3390\/rs16142610","type":"journal-article","created":{"date-parts":[[2024,7,17]],"date-time":"2024-07-17T15:15:19Z","timestamp":1721229319000},"page":"2610","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Complementary-View SAR Target Recognition Based on One-Shot Learning"],"prefix":"10.3390","volume":"16","author":[{"given":"Benteng","family":"Chen","sequence":"first","affiliation":[{"name":"School of International Education, Beijing University of Chemical Technology, Beijing 100029, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-3333-9816","authenticated-orcid":false,"given":"Zhengkang","family":"Zhou","sequence":"additional","affiliation":[{"name":"School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, China"}]},{"given":"Chunyu","family":"Liu","sequence":"additional","affiliation":[{"name":"College of Electronic Information and Optical Engineering, Nankai University, Tianjin 300072, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3252-9948","authenticated-orcid":false,"given":"Jia","family":"Zheng","sequence":"additional","affiliation":[{"name":"National Key Laboratory of Radar Signal Processing, Xidian University, Xi\u2019an 710071, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,7,17]]},"reference":[{"key":"ref_1","first-page":"8","article-title":"Mercury Systems Debuts Synthetic Aperture Radar Test Bed","volume":"108","author":"Magnuson","year":"2024","journal-title":"Natl. 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