Abstract
Recently, deep learning methods have been applied to ship classification in Synthetic Aperture Radar (SAR) images. However, because of the problem of imbalanced and insufficient samples in the SAR ship datasets, accurately identifying SAR ships still poses challenges. In this paper, we propose an improved T2T-ViT model based on the latent diffusion model, which expands the data set through image generation, and adds the SE attention mechanism to adjust the channel weight. To evaluate the effectiveness of the proposed method, training and experiments were conducted on the OpenSARShip 2.0 dataset. Our proposed model, in accordance with experimental results, achieves better recognition accuracy compared with existing models.
This work was supported in part by the National Natural Science Foundation of China under Grant 62271162 and 61971153, and Natural Science Foundation of Heilongjiang Province (YQ2022E016).
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Qi, Y., Wang, L., Li, K., Liu, H., Zhao, C. (2024). Latent Diffusion Model-Based T2T-ViT for SAR Ship Classification. In: Sun, Y., Lu, T., Wang, T., Fan, H., Liu, D., Du, B. (eds) Computer Supported Cooperative Work and Social Computing. ChineseCSCW 2023. Communications in Computer and Information Science, vol 2013. Springer, Singapore. https://doi.org/10.1007/978-981-99-9640-7_22
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DOI: https://doi.org/10.1007/978-981-99-9640-7_22
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