{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,10]],"date-time":"2026-02-10T18:35:11Z","timestamp":1770748511580,"version":"3.50.0"},"reference-count":40,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2022,9,19]],"date-time":"2022-09-19T00:00:00Z","timestamp":1663545600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Inter-University Consortium for Telecommunications (CNIT)"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Most of the existing Non-Cooperative Target Recognition (NCTR) systems follow the \u201cclosed world\u201d assumption, i.e., they only work with what was previously observed. Nevertheless, the real world is relatively \u201copen\u201d in the sense that the knowledge of the environment is incomplete. Therefore, unknown targets can feed the recognition system at any time while it is operational. Addressing this issue, the Openmax classifier has been recently proposed in the optical domain to make convolutional neural networks (CNN) able to reject unknown targets. There are some fundamental limitations in the Openmax classifier that can end up with two potential errors: (1) rejecting a known target and (2) classifying an unknown target. In this paper, we propose a new classifier to increase the robustness and accuracy. The proposed classifier, which is inspired by the limitations of the Openmax classifier, is based on proportional similarity between the test image and different training classes. We evaluate our method by radar images of man-made targets from the Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset. Moreover, a more in-depth discussion on the Openmax hyper-parameters and a detailed description of the Openmax functioning are given.<\/jats:p>","DOI":"10.3390\/rs14184665","type":"journal-article","created":{"date-parts":[[2022,9,20]],"date-time":"2022-09-20T04:28:55Z","timestamp":1663648135000},"page":"4665","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":23,"title":["Proportional Similarity-Based Openmax Classifier for Open Set Recognition in SAR Images"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5278-6509","authenticated-orcid":false,"given":"Elisa","family":"Giusti","sequence":"first","affiliation":[{"name":"National Laboratory of Radar and Surveillance Systems (RaSS), National Inter-University Consortium for Telecommunication (CNIT), 56124 Pisa, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9894-0839","authenticated-orcid":false,"given":"Selenia","family":"Ghio","sequence":"additional","affiliation":[{"name":"National Laboratory of Radar and Surveillance Systems (RaSS), National Inter-University Consortium for Telecommunication (CNIT), 56124 Pisa, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6057-8459","authenticated-orcid":false,"given":"Amir Hosein","family":"Oveis","sequence":"additional","affiliation":[{"name":"National Laboratory of Radar and Surveillance Systems (RaSS), National Inter-University Consortium for Telecommunication (CNIT), 56124 Pisa, Italy"},{"name":"Department of Information Engineering, University of Pisa, 56126 Pisa, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8985-5069","authenticated-orcid":false,"given":"Marco","family":"Martorella","sequence":"additional","affiliation":[{"name":"National Laboratory of Radar and Surveillance Systems (RaSS), National Inter-University Consortium for Telecommunication (CNIT), 56124 Pisa, Italy"},{"name":"Department of Information Engineering, University of Pisa, 56126 Pisa, Italy"}]}],"member":"1968","published-online":{"date-parts":[[2022,9,19]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"46","DOI":"10.1109\/MAES.2019.2933972","article-title":"Deep Learning for Classification of Mini-UAVs Using Micro-Doppler Spectrograms in Cognitive Radar","volume":"34","author":"Huizing","year":"2019","journal-title":"IEEE Aerosp. 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