Point-Based Weakly Supervised Learning for Object Detection in High Spatial Resolution Remote Sensing Images

Object detection is challenging in high spatial resolution (HSR) remote sensing images that have a complex background and irregular object locations. To minimize manual annotation cost in supervised learning methods and achieve advanced detection performance, we proposed a point-based weakly supervised learning method to address the object detection challenge in HSR remote sensing images. In the study, point labels are introduced to guide candidate bounding box mining and generate pseudobounding boxes for objects. Then, pseudobounding boxes are applied to train the detection model. A progressive candidate bounding box mining strategy is proposed to refine object detection. Experiments are conducted on a comprehensive HSR dataset which contains four categories. Results indicate the proposed method achieves competitive performance compared to YOLOv5 which is trained on manual bounding box annotations. In comparison to the state-of-the-art weakly supervised learning method, our method outperforms WSDDN method with 0.62 mean average precision score.