Active-SAOOD: Active Sparsely Annotated Oriented Object Detection in Remote Sensing Images

Sparse annotation has emerged as an effective strategy for reducing the annotation burden of oriented object detection in remote sensing images. However, with a limited annotation budget, selecting informative instances becomes critical, while random selection may allocate annotations to less valuable instances. To this end, we propose an active learning-based sparsely annotated oriented object detection (SAOOD) method, termed Active-SAOOD, which actively selects high-value instances for annotation. Specifically, existing image-level active learning methods face two limitations in SAOOD task: (1) the granularity mismatch in annotation value evaluation and (2) unstable training caused by excessive selection of difficult samples without considering model states. To overcome these challenges, we design instance-level evaluation dimensions tailored to SAOOD, enabling the selection of instances that improve both fundamental detection and representation generalization abilities. Moreover, a model-state observation strategy is introduced to identify instances that better match the current learning state of model, thereby improving training stability. Extensive experiments on multiple datasets demonstrate that the Active-SAOOD consistently improves detection performance and training stability under different sparse annotation ratios, highlighting its practical value for SAOOD. The code will be public.