PL-FSOD: Pseudo-Labeling for Few-Shot Remote Sensing Object Detection—Mitigating Annotation Gaps and Forgetting
Chao Li, Enyue Ji, Bingying Yao, Shenzhi Li, Peng Wang · IEEE Access · 2025
Few-shot object detection (FSOD) in remote sensing images(RSIs), unlike those in natural images, is highly challenging yet critical.Key hurdles include difficulties and incomplete annotations, drastic scale variations, and high inter-class similarity. Existing methods often underutilize K-shot support samples, perform poorly on novel categories, suffer from overfitting that degrades base-class performance, and lack efficacy for small objects. To address these, we propose Pseudo-Labeling FSOD (PL-FSOD) based on a teacher-student network.We design an Attention Path Aggregation Feature Pyramid Network (APAFPN) to boost small-object detection via multi-scale attention fusion. The Multi-Branch Region Proposal Network (MBRPN) and Pseudo-Labeling Bounding Box Head (PLBBH) enhance proposal quality and novel-object recall. Additionally, a metric-based loss function strengthens classifier discriminative power.Experiments on DIOR, NWPU VHR10.v2 show our method outperforms best FSOD approaches, validating the effectiveness of all designed modules.