DTRB-Net: a dual-teacher network with adaptive reliable bank for SAR image classification

Abstract Ship classification in synthetic aperture radar (SAR) imagery plays a critical role in many practical applications. However, existing semi-supervised frameworks still suffer from unstable pseudo-label quality and insufficient utilization of unlabeled data. In this article, we propose a dual-teacher network with an adaptive reliable bank (DTRB-Net), a semi-supervised learning framework that integrates a dual-teacher architecture with an adaptive reliable bank to address these challenges. The teacher network and its subnetwork collaboratively generate pseudo-label pairs that are then filtered through a two-stage selection strategy based on pseudo-label confidence and prediction discrepancy to ensure reliability. These high-quality pseudo-label pairs are stored and dynamically updated in a class-wise adaptive reliable bank, providing stable contrastive samples for the student network, and enabling more effective exploitation of unlabeled data. In addition, we design a new loss function that jointly leverages labeled and unlabeled data to enhance the student network’s feature learning capability. Extensive experiments on the FUSAR-Ship and OpenSARShip datasets show that DTRB-Net achieves superior accuracy on both three- and six-class ship classification tasks compared with existing methods, demonstrating the effectiveness and robustness of the proposed framework.