Scattering Characteristics-guided Self-supervised Learning for Target Classification in SAR Images

With the advancement of science and technology, the spatial resolution of spaceborne Synthetic Aperture Radar (SAR) images has achieved sub-meter precision, enabling the classification and identification of targets. Notably, the rapid and accurate classification of aircraft targets using SAR images has become a significant application requirement. Nevertheless, challenges persist in the classification of targets within aircraft remote sensing images. This paper introduces a novel method aimed at enhancing the scattering characteristics in SAR images to address issues associated with discrete imaging pixels, strong scattering characteristics, and the consequent difficulty in distinguishing aircraft features. The proposed method systematically correlates the discrete SAR image scattering characteristics, thereby facilitating the extraction of characteristic information pertaining to the edge contours of aircraft targets. Recognizing the limitations of traditional supervised classification methods, which often demand substantial manually labeled information, this study integrates self-supervised contrastive learning to mitigate labeling costs. Additionally, acknowledging the imbalanced distribution of samples across different categories and the prevalent "long tail"effect, a weighted loss function is introduced to rectify the imbalance and enhance the network's focus on the learning of underrepresented samples. The efficacy of the proposed method is evaluated using a self-established dataset. The results demonstrate a 1.48% increase in accuracy compared to the original self-supervised method, indicating an improvement in the classification performance for categories characterized by an imbalanced sample distribution.