A fine-grained SAR target recognition method based on bilinear fusion

Zhicheng Chen; Youquan Lin; Long Zhuang; Hui Yu

doi:10.1117/12.2673604

22 May 2023 A fine-grained SAR target recognition method based on bilinear fusion

Zhicheng Chen, Youquan Lin, Long Zhuang, Hui Yu

Proceedings Volume 12640, International Conference on Internet of Things and Machine Learning (IoTML 2022); 126400Q (2023) https://doi.org/10.1117/12.2673604
Event: International Conference on Internet of Things and Machine Learning (IoTML 2022), 2022, Harbin, China

Abstract

The models used in most deep neural networks SAR object identification algorithms are often designed based on coarse-grained recognition tasks and are not well adapted to the task requirements of SAR vehicle target recognition. To further enhance the accuracy of identification, this article introduces the fine-grained recognition idea into SAR target recognition and designs a bilinear feature fusion convolution module with attention allocation capability. The deep features are first extracted using the residual network, then the attention module is connected to screen the channel information. Finally, the information fusion of the two streams is achieved, and the fused features are used to complete the recognition task. The model is validated on the public dataset. Comparative experiments show our innovative algorithm has a great performance improvement over other classical target recognition methods. The visualization features show that the features mined by this model has better interpretable.

Citation Download Citation

Zhicheng Chen, Youquan Lin, Long Zhuang, and Hui Yu "A fine-grained SAR target recognition method based on bilinear fusion", Proc. SPIE 12640, International Conference on Internet of Things and Machine Learning (IoTML 2022), 126400Q (22 May 2023); https://doi.org/10.1117/12.2673604

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