Semi-Supervised Object Detection for Remote Sensing Images Using Consistent Dense Pseudo-Labels

Semi-supervised learning aims to improve the generalization performance of a model by exploiting the large quantity of unlabeled data together with limited labeled data during training. When applied to object detection in remote sensing images, semi-supervised learning can not only effectively alleviate the time-consuming and costly labeling of bounding boxes but also improve the performance and generalization of corresponding object detection methods. However, most current semi-supervised learning-based object detection methods (especially combined with pseudo-labels) for remote sensing images ignore a key issue, that is, the consistency of pseudo-labels. In this paper, a novel semi-supervised learning-based method for object detection in remote sensing images called CDPL is proposed, which includes an adaptive mechanism that directly incorporates the potential object information into the dense pseudo-label selection process and carefully selects the appropriate dense pseudo-labels in the scenes where objects are densely distributed. CDPL consists of two main components: feature-aligned dense pseudo-label selection and sparse pseudo-label-based regression object alignment. The experimental results for typical remote sensing datasets show that the proposed method results in a satisfactory performance improvement.