Uncertainty Quantification for Semi-Supervised Object Detection in Remote Sensing Images

Abstract

Semi-supervised object detection (SSOD) aims to solve the data annotation challenge in object detection and can achieve remarkable progress in natural scenes; however, it remains unexplored in horizontal bounding box (HBB)-based remote sensing imagery where annotation tasks pose greater challenges. In remote sensing scenarios, objects exhibit arbitrary orientations, small scales, and dense distributions, leading to pseudoboxes with fuzzy boundaries and class imbalance issues. Therefore, we propose UNCertainty quantification (UNC) for SSOD in remote sensing images. UNC uses uncertainty to guide the network from both regression and classification perspectives: Semantic alignment SAM calibration (SASC) uses pseudoboxes as box prompts for the input of the segment anything model (SAM), achieving more precise boundaries. Subsequently, boundaries with lower regression uncertainty are selected as the final pseudoboxes, ensuring better alignment between the pseudoboxes and the ground truth. Dynamic uncertainty weighting (DUW) calculates class uncertainty and determines its correlation with the availability of instances per class. High uncertainty implies limited availability of instances, necessitating greater emphasis on instances of that class. Furthermore, we set a percentage uncertainty threshold to avoid overemphasis caused by individual classes. Extensive experiments conducted on the DIOR and DOTA HBB-based datasets demonstrate the effectiveness of our method in leveraging unlabeled image information. Specifically, compared with the supervised baseline method, the UNC method improves mAP by 12.4% and 8.6% when 5% and 10% of labeled data on DIOR, respectively.