Synthetic Data Generation Based on Multistage Blending Strategy for Airport Runway Detection in Areas of Heterogeneous Land Cover From Remote Sensing Images

Global land cover heterogeneity is usually directly reflected in the background of remote sensing objects. This substantially affects the generalization ability of object detection models and is a challenge for remote sensing object detection in large-scale areas. In this study, we analyze the heterogeneity of land cover in different climate areas and propose a remote sensing object image synthesis method based on multistage blending strategy (MBS) to achieve a high-quality blending of the labeled runway foreground image and the typical surface image of the area to be detected. Three geometric augmentation algorithms are applied to expand the size of the sample data. Finally, two representative object detection models, YOLOv5 and Faster R-CNN, are used to evaluate the effectiveness of the synthetic data. Experiments in North Africa and Central Asia demonstrate that the MBS method effectively improves the performance of airport runway detection in large-scale heterogeneous areas. Additionally, our method performs better than Copy-Paste and Poisson blending.