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A Tri-Model SAR Semi-Supervised Recognition Method Based on Attention-Augmented Convolutional Networks

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posted on 2023-10-19, 13:30 authored by S Yan, Y Zhang, F Gao, J Sun, A Hussain, Huiyu Zhou

 Semisupervised learning in synthetic aperture radars (SARs) is one of the research hotspots in the field of radar image automatic target recognition. It can efficiently deal with challenging environments where there are insufficient labeled samples and large unlabeled samples in the SAR dataset. In recent years, consistency regularization methods in semisupervised learning have shown considerable improvement in recognition accuracy and efficiency. Current consistency regularization approaches suffer from two main shortcomings: first, extracting all of the relevant information in the image target is difficult owing to the inability of conventional convolutional neural networks to capture global relational information; second, the standard teacher–student regularization methodology causes confirmation biases due to the high coupling between teacher and student models. This article adopts an innovative trimodel semisupervised method based on attention-augmented convolutional networks to address the aforementioned obstacles. Specifically, we develop an attention mechanism incorporating a novel positional embedding method based on recurrent neural networks and integrate this with a standard convolutional network as a feature extractor, to improve the network's ability to extract global feature information from images. Furthermore, we address the confirmation bias problem by introducing a classmate model to the standard teacher–student structure and utilize the model to impose a weak consistency constraint designed on the student to weaken the strong coupling between the teacher and the student. Comparative experiments on the Moving and Stationary Target Acquisition and Recognition dataset show that our method outperforms state-of-the-art semisupervised methods in terms of recognition accuracy, demonstrating its potential as a new benchmark approach for the deep learning and SAR research community. 

Funding

The work of Amir Hussain was supported by the U.K. Engineering and Physical Sciences Research Council under Grant EP/M026981/1, Grant EP/T021063/1, and Grant EP/T024917/1. The work of Huiyu Zhou was supported in part by the Royal Society Newton Advanced Fellowship under Grant NA160342 and in part by the European Union’s Horizon 2020 Research and Innovation Program under Marie Sklodowska Curie Grant 720325. This work was supported in part by the National Natural Science Foundation of China under Grant 61771027 and Grant 61071139.

History

Citation

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 15, pp. 9566-9583, 2022

Author affiliation

Department of Informatics

Version

  • VoR (Version of Record)

Published in

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing

Volume

15

Pagination

9566-9583

Publisher

Institute of Electrical and Electronics Engineers

issn

1939-1404

eissn

2151-1535

Acceptance date

2022-10-26

Copyright date

2022

Available date

2023-10-19

Language

en

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