Multi-Stage Semi-Supervised Transformer for Remote Sensing Semantic Segmentation with Various Data Augmentation

Existing research in semantic segmentation heavily relies on numerous manually annotated data, while the vast amount of unlabeled data still needs to be fully utilized. To address this challenge, this paper introduces a novel multi-stage semi-supervised method for remote sensing semantic segmentation, building upon a modified classical self-training scheme that leverages pseudo-labels. By dividing the data augmentation process into two stages, we employ various data augmentation strategies and balance the size of labels and pseudo-labels validated through rigorous experimentation, which can alleviate the student model from overfitting pseudo-labels. Furthermore, we also explore the efficacy of the Vision Transformer model in semi-supervised semantic segmentation, leading to further performance enhancements. The experimental results show that our semi-supervised remote sensing semantic segmentation method exhibits a more intuitive structure, easier deployment, and superior performance.