RemoteDPL: A Semi-Supervised Object Detector With Dense Pseudo-Labels for Remote Sensing

Deep learning-based object detection has seen substantial advancements, however, its practical deployment is often constrained by the need for large-scale labeled datasets. This limitation becomes even more critical in remote sensing imagery, where objects are densely distributed and exhibit significant scale variations. To address these challenges, we introduce RemoteDPL, a novel semi-supervised object detection (SSOD) framework that leverages dense pseudo-labeling (DPL) and multi-scale learning. RemoteDPL offers three key contributions. First, a fusion module is designed to dynamically integrate spatial and channel features across scales, improving detection across varied object sizes. Second, an instance density prediction branch is introduced to support pseudo-label mining, enhancing detection performance in densely populated regions. Lastly, we propose a two-stage pseudo-label filtering strategy that first selects "pending" class predictions and then refines them using a joint confidence score based on both classification and density information. Extensive experiments on the DOTA-v1.0 and NWPU datasets confirm the effectiveness of RemoteDPL, demonstrating its clear advantage over existing state-of-the-art (SOTA) semi-supervised object detection methods. On the NWPU dataset, RemoteDPL outperforms the SOTA baseline by +3.44%, +1.10%, and +1.62% under the settings of data labelled with 30%, 40%, and 50%, respectively, highlighting its strong capability in low-label remote sensing scenarios.