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ORIGINAL RESEARCH article

Front. Earth Sci.
Sec. Atmospheric Science
Volume 12 - 2024 | doi: 10.3389/feart.2024.1469032

Few-shot SAR Target Classification via Meta-Learning with Hybrid Models

Provisionally accepted
Qingtian Geng Qingtian Geng Yaning Wang Yaning Wang Qingliang Li Qingliang Li *
  • Department of Computer Science and Technology, Changchun Normal University, Changchun, China

The final, formatted version of the article will be published soon.

    Currently, in Synthetic Aperture Radar Automatic Target Recognition (SAR ATR), few-shot methods can save cost and resources while enhancing adaptability. However, due to the limitations of SAR imaging environments and observation conditions, obtaining a large amount of high-value target data is challenging, leading to a severe shortage of datasets. This paper proposes the use of an Adaptive Dynamic Weight Hybrid Model (ADW-HM) meta-learning framework to address the problem of poor recognition accuracy for unknown classes caused by sample constraints. By dynamically weighting and learning model parameters independently, the framework dynamically integrates model results to improve recognition accuracy for unknown classes. Experiments conducted on the TASK-MSTAR and OpenSARShip datasets demonstrate that the ADW-HM framework can obtain more comprehensive and integrated feature representations, reduce overfitting, and enhance generalization capability for unknown classes. The accuracy is improved in both 1-shot and 5-shot scenarios, indicating that ADW-HM is feasible for addressing few-shot problems.

    Keywords: Few-shot learning(FSL), Adaptive Dynamic Weight Hybrid Model, Synthetic Aperture Radar, Automatic target recognition, meta-learning

    Received: 23 Jul 2024; Accepted: 06 Nov 2024.

    Copyright: © 2024 Geng, Wang and Li. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Qingliang Li, Department of Computer Science and Technology, Changchun Normal University, Changchun, China

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.