{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,13]],"date-time":"2025-10-13T15:37:32Z","timestamp":1760369852016,"version":"build-2065373602"},"reference-count":52,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2022,9,14]],"date-time":"2022-09-14T00:00:00Z","timestamp":1663113600000},"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":["61771027","EP\/M026981\/1","EP\/T021063\/1","EP\/T024917\/1","NA160342","720325"],"award-info":[{"award-number":["61771027","EP\/M026981\/1","EP\/T021063\/1","EP\/T024917\/1","NA160342","720325"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"UK Engineering and Physical Sciences Research Council (EPSRC)","award":["61771027","EP\/M026981\/1","EP\/T021063\/1","EP\/T024917\/1","NA160342","720325"],"award-info":[{"award-number":["61771027","EP\/M026981\/1","EP\/T021063\/1","EP\/T024917\/1","NA160342","720325"]}]},{"name":"Royal Society\u2019s Newton Advanced Fellowship","award":["61771027","EP\/M026981\/1","EP\/T021063\/1","EP\/T024917\/1","NA160342","720325"],"award-info":[{"award-number":["61771027","EP\/M026981\/1","EP\/T021063\/1","EP\/T024917\/1","NA160342","720325"]}]},{"name":"European Union\u2019s Horizon 2020 research and innovation program","award":["61771027","EP\/M026981\/1","EP\/T021063\/1","EP\/T024917\/1","NA160342","720325"],"award-info":[{"award-number":["61771027","EP\/M026981\/1","EP\/T021063\/1","EP\/T024917\/1","NA160342","720325"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Convolutional Neural Network (CNN) has been widely applied in the field of synthetic aperture radar (SAR) image recognition. Nevertheless, CNN-based recognition methods usually encounter the problem of poor feature representation ability due to insufficient labeled SAR images. In addition, the large inner-class variety and high cross-class similarity of SAR images pose a challenge for classification. To alleviate the problems mentioned above, we propose a novel few-shot learning (FSL) method for SAR image recognition, which is composed of the multi-feature fusion network (MFFN) and the weighted distance classifier (WDC). The MFFN is utilized to extract input images\u2019 features, and the WDC outputs the classification results based on these features. The MFFN is constructed by adding a multi-scale feature fusion module (MsFFM) and a hand-crafted feature insertion module (HcFIM) to a standard CNN. The feature extraction and representation capability can be enhanced by inserting the traditional hand-crafted features as auxiliary features. With the aid of information from different scales of features, targets of the same class can be more easily aggregated. The weight generation module in WDC is designed to generate category-specific weights for query images. The WDC distributes these weights along the corresponding Euclidean distance to tackle the high cross-class similarity problem. In addition, weight generation loss is proposed to improve recognition performance by guiding the weight generation module. Experimental results on the Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset and the Vehicle and Aircraft (VA) dataset demonstrate that our proposed method surpasses several typical FSL methods.<\/jats:p>","DOI":"10.3390\/rs14184583","type":"journal-article","created":{"date-parts":[[2022,9,14]],"date-time":"2022-09-14T23:16:36Z","timestamp":1663197396000},"page":"4583","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["A Few-Shot Learning Method for SAR Images Based on Weighted Distance and Feature Fusion"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1489-0812","authenticated-orcid":false,"given":"Fei","family":"Gao","sequence":"first","affiliation":[{"name":"School of Electronic and Information Engineering, Beihang University, Beijing 100191, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6472-4599","authenticated-orcid":false,"given":"Jingming","family":"Xu","sequence":"additional","affiliation":[{"name":"School of Electronic and Information Engineering, Beihang University, Beijing 100191, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rongling","family":"Lang","sequence":"additional","affiliation":[{"name":"School of Electronic and Information Engineering, Beihang University, Beijing 100191, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jun","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Electronic and Information Engineering, Beihang University, Beijing 100191, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8080-082X","authenticated-orcid":false,"given":"Amir","family":"Hussain","sequence":"additional","affiliation":[{"name":"Cyber and Big Data Research Laboratory, Edinburgh Napier University, Edinburgh EH11 4BN, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Huiyu","family":"Zhou","sequence":"additional","affiliation":[{"name":"Department of Informatics, University of Leicester, Leicester LE1 7RH, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,9,14]]},"reference":[{"doi-asserted-by":"crossref","unstructured":"Zheng, T., Wang, J., and Lei, P. 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