Pixel-Level Self-Supervised Learning for Semi-Supervised Building Extraction From Remote Sensing Images

The building extraction from remote sensed images ash been a challenging yet vital task for applicable purposes such as urban monitoring and cartography. Most of the existing learning based approaches focus on the supervised building extraction methods, of which the models should be trained with images and the corresponding labels. This research exploits a self-supervised approach for building extraction, which could train the backbone within a building extraction network without annotations. Specifically, the backbone is initially trained with a pixel-level self-supervised module instead of commonly used supervised approaches or instance-level self-supervised modules. Next, the pretrained backbone is embedded into a task-specific network followed by tuning with limited annotations. The experiments were conducted on three popular datasets and the results show that our method achieves improvements regarding both intersection over union (IoU) and F1-score compared to supervised approach and instance-level self-supervised methods. Our study thus confirms the potential of pixel-level self-supervised approach for semantic segmentation for remote sensing images.