Semi-supervised Object Detection for Remote Sensing Images via Pseudo Labeling and Consistency Learning

Abstract

Object detection for remote sensing images (RSI) has made significant progress in recent years. However, existing supervised methods typically require a large amount of labeled data, whose acquisition, and particularly that of RSI, can be quite expensive. For natural images, many semi-supervised methods have been proposed to tackle the lack of labeled data, while few studies have focused on semi-supervised object detection for RSI. To address this problem, this paper proposes RS-PCL, which is a semi-supervised method applying Pseudo labeling and Consistency Learning to RSI. The RS-PCL method uses the teacher-student training scheme to generate high-quality pseudo labels for self-training. To deal with biased pseudo boxes, Noisy Pseudo box Assignment (NPA) is proposed to consider the teacher predictions during the label assignments of the unlabeled student. To ensure scale invariance, Scale-Invariant Learning (SIL) is proposed to carry out feature-consistency training on multi-scale images. This study conducted experiments using the DOTA-v1.0 dataset as labeled data and newly added images in the DOTA-v2.0 dataset (DOTA-v2.0 was constructed by adding extra images to DOTA-v1.0) as unlabeled data. The results reveal that there is obvious improvement over the supervised baseline and state-of-the-art semi-supervised methods, which demonstrates the effectiveness of the proposed method.