{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T05:23:21Z","timestamp":1761110601201,"version":"build-2065373602"},"reference-count":33,"publisher":"MDPI AG","issue":"13","license":[{"start":{"date-parts":[[2021,6,24]],"date-time":"2021-06-24T00:00:00Z","timestamp":1624492800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012659","name":"Foundation for Innovative Research Groups of the National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61701478"],"award-info":[{"award-number":["61701478"]}],"id":[{"id":"10.13039\/501100012659","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>At present, synthetic aperture radar (SAR) automatic target recognition (ATR) has been deeply researched and widely used in military and civilian fields. SAR images are very sensitive to the azimuth aspect of the imaging geomety; the same target at different aspects differs greatly. Thus, the multi-aspect SAR image sequence contains more information for classification and recognition, which requires the reliable and robust multi-aspect target recognition method. Nowadays, SAR target recognition methods are mostly based on deep learning. However, the SAR dataset is usually expensive to obtain, especially for a certain target. It is difficult to obtain enough samples for deep learning model training. This paper proposes a multi-aspect SAR target recognition method based on a prototypical network. Furthermore, methods such as multi-task learning and multi-level feature fusion are also introduced to enhance the recognition accuracy under the case of a small number of training samples. The experiments by using the MSTAR dataset have proven that the recognition accuracy of our method can be close to the accruacy level by all samples and our method can be applied to other feather extraction models to deal with small sample learning problems.<\/jats:p>","DOI":"10.3390\/s21134333","type":"journal-article","created":{"date-parts":[[2021,6,24]],"date-time":"2021-06-24T23:22:14Z","timestamp":1624576934000},"page":"4333","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Multi-Aspect SAR Target Recognition Based on Prototypical Network with a Small Number of Training Samples"],"prefix":"10.3390","volume":"21","author":[{"given":"Pengfei","family":"Zhao","sequence":"first","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100194, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100190, China"},{"name":"Key Laboratory of Technology in Geo-Spatial Information Processing and Application System Chinese Academy of Sciences, Beijing 100194, China"}]},{"given":"Lijia","family":"Huang","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100194, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100190, China"},{"name":"Key Laboratory of Technology in Geo-Spatial Information Processing and Application System Chinese Academy of Sciences, Beijing 100194, China"}]},{"given":"Yu","family":"Xin","sequence":"additional","affiliation":[{"name":"Beijing Institute of Remote Sensing Information, Beijing 100192, China"}]},{"given":"Jiayi","family":"Guo","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100194, China"},{"name":"Key Laboratory of Technology in Geo-Spatial Information Processing and Application System Chinese Academy of Sciences, Beijing 100194, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5041-3300","authenticated-orcid":false,"given":"Zongxu","family":"Pan","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100194, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100190, China"},{"name":"Key Laboratory of Technology in Geo-Spatial Information Processing and Application System Chinese Academy of Sciences, Beijing 100194, China"}]}],"member":"1968","published-online":{"date-parts":[[2021,6,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"364","DOI":"10.1109\/TAES.1986.310772","article-title":"Automatic target recognition: State of the art survey","volume":"4","author":"Bhanu","year":"1986","journal-title":"IEEE Trans. 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