Weighted Pseudo-Labels and Bounding Boxes for Semisupervised SAR Target Detection

Abstract

Synthetic aperture radar (SAR) image target detection methods based on semisupervised learning, such as the mean teacher framework, have shown promise in diminishing the issue of limited labeled data. However, several challenges exist in current methods. First, data augmentation techniques designed for optical images may not be suitable for SAR images due to differences in imaging methods. In addition, the contribution of pseudo labels remains constant during the initial retraining stage can lead to degradation in prediction results. Moreover, the low quality of predicted bounding boxes poses a challenge for effective retraining. To address these challenges, we propose an end-to-end semisupervised detection method based on the mean teacher framework. To enhance the robustness of training, we first introduce SAR-specific data augmentation techniques, including multiplicative noise, which effectively increase the diversity of training samples. Second, we propose a method that weights the losses of pseudo-labeled data using a hard-sigmoid function, gradually improving the importance of pseudo-labeled data during retraining, thereby alleviating their potential negative impact on the training process. Finally, we propose an IoU-aware subnetwork to incorporate high-quality pseudo-labeled bounding boxes into retraining, allowing them to contribute to network adjustments while mitigating the impact of low-quality samples. Experimental evaluations on publicly available SAR image datasets demonstrate the effectiveness of our proposed method in improving the target detection capability of semisupervised SAR target detection.

Keywords

• Convolutional neural networks (CNNs)
• deep learning
• object detection
• semisupervised learning
• synthetic aperture radar (SAR)