{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,10]],"date-time":"2026-07-10T23:41:00Z","timestamp":1783726860881,"version":"3.55.0"},"reference-count":47,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2022,4,14]],"date-time":"2022-04-14T00:00:00Z","timestamp":1649894400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Few-shot synthetic aperture radar automatic target recognition (SAR-ATR) aims to recognize the targets of the images (query images) based on a few annotated images (support images). Such a task requires modeling the relationship between the query and support images. In this paper, we propose the instance-aware transformer (IAT) model. The IAT exploits the power of all instances by constructing the attention map based on the similarities between the query feature and all support features. The query feature aggregates the support features based on the attention values. To align the features of the query and support images in IAT, the shared cross-transformer keep all the projections in the module shared across all features. Instance cosine distance is used in training to minimize the distance between the query feature and the support features. In testing, to fuse the support features of the same class into the class representation, Euclidean (Cosine) Loss is used to calculate the query-class distances. Experiments on the two proposed few-shot SAR-ATR test sets based on MSTAR demonstrate the superiority of the proposed method.<\/jats:p>","DOI":"10.3390\/rs14081884","type":"journal-article","created":{"date-parts":[[2022,4,19]],"date-time":"2022-04-19T02:39:31Z","timestamp":1650335971000},"page":"1884","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["Few-Shot SAR-ATR Based on Instance-Aware Transformer"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0515-8363","authenticated-orcid":false,"given":"Xin","family":"Zhao","sequence":"first","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China"},{"name":"Key Laboratory of Technology in Geo-Spatial Information Processing and Application System, Beijing 100190, China"},{"name":"School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 101408, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaoling","family":"Lv","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China"},{"name":"Key Laboratory of Technology in Geo-Spatial Information Processing and Application System, Beijing 100190, China"},{"name":"School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 101408, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jinlei","family":"Cai","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China"},{"name":"Key Laboratory of Technology in Geo-Spatial Information Processing and Application System, Beijing 100190, China"},{"name":"School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 101408, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiayi","family":"Guo","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China"},{"name":"Key Laboratory of Technology in Geo-Spatial Information Processing and Application System, Beijing 100190, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yueting","family":"Zhang","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China"},{"name":"Key Laboratory of Technology in Geo-Spatial Information Processing and Application System, Beijing 100190, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8517-3415","authenticated-orcid":false,"given":"Xiaolan","family":"Qiu","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China"},{"name":"Key Laboratory of Technology in Geo-Spatial Information Processing and Application System, Beijing 100190, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yirong","family":"Wu","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China"},{"name":"Key Laboratory of Technology in Geo-Spatial Information Processing and Application System, Beijing 100190, China"},{"name":"School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 101408, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,4,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"94750F","DOI":"10.1117\/12.2176558","article-title":"Deep convolutional neural networks for ATR from SAR imagery","volume":"Volume 9475","author":"Morgan","year":"2015","journal-title":"Algorithms for Synthetic Aperture Radar Imagery XXII"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Shao, J., Qu, C., and Li, J. 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