General Feature Extraction In SAR Target Classification: A Contrastive Learning Approach Across Sensor Types
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arXiv
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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866915134437326848 |
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| author | Muzeau, M. Frontera-Pons, J. Ren, Chengfang Ovarlez, J. -P. |
| author_facet | Muzeau, M. Frontera-Pons, J. Ren, Chengfang Ovarlez, J. -P. |
| contents | The increased availability of SAR data has raised a growing interest in applying deep learning algorithms. However, the limited availability of labeled data poses a significant challenge for supervised training. This article introduces a new method for classifying SAR data with minimal labeled images. The method is based on a feature extractor Vit trained with contrastive learning. It is trained on a dataset completely different from the one on which classification is made. The effectiveness of the method is assessed through 2D visualization using t-SNE for qualitative evaluation and k-NN classification with a small number of labeled data for quantitative evaluation. Notably, our results outperform a k-NN on data processed with PCA and a ResNet-34 specifically trained for the task, achieving a 95.9% accuracy on the MSTAR dataset with just ten labeled images per class. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_01162 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | General Feature Extraction In SAR Target Classification: A Contrastive Learning Approach Across Sensor Types Muzeau, M. Frontera-Pons, J. Ren, Chengfang Ovarlez, J. -P. Signal Processing The increased availability of SAR data has raised a growing interest in applying deep learning algorithms. However, the limited availability of labeled data poses a significant challenge for supervised training. This article introduces a new method for classifying SAR data with minimal labeled images. The method is based on a feature extractor Vit trained with contrastive learning. It is trained on a dataset completely different from the one on which classification is made. The effectiveness of the method is assessed through 2D visualization using t-SNE for qualitative evaluation and k-NN classification with a small number of labeled data for quantitative evaluation. Notably, our results outperform a k-NN on data processed with PCA and a ResNet-34 specifically trained for the task, achieving a 95.9% accuracy on the MSTAR dataset with just ten labeled images per class. |
| title | General Feature Extraction In SAR Target Classification: A Contrastive Learning Approach Across Sensor Types |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2502.01162 |