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.
@article{arxiv.2502.01162,
title = {General Feature Extraction In SAR Target Classification: A Contrastive Learning Approach Across Sensor Types},
author = {M. Muzeau and J. Frontera-Pons and Chengfang Ren and J. -P. Ovarlez},
journal= {arXiv preprint arXiv:2502.01162},
year = {2025}
}