Hierarchical Augmentation and Region-Aware Contrastive Learning for Semi-Supervised Semantic Segmentation of Remote Sensing Images

Semi-supervised semantic segmentation has gained significant attention as a method to reduce the substantial expense associated with pixel-level labeling. The existing methods primarily rely on consistency regularization or self-training. Recent consistency regularization methods augment the input images with weak or strong augmentation (SA) to improve the performance. However, such simple augmentations are not sufficient to simulate the variations in remote sensing images. The self-training methods exclude the noisy pseudo labels by some selection from the unsupervised training process to obtain better performance. The selection may lead to semantic information loss and bias of latent distribution. To solve the above two problems, we propose hierarchical augmentation (HA) and region-aware contrastive (RC) learning, namely HARC, for remote sensing images. The HA strategy simulates three levels of remote sensing image variations, i.e., spatial variations, uniform spectral variations, and uneven spectral variations. It can significantly enhance the model’s capability to handle more intricate variations. The RC learning learns a class-wise feature distribution of all unlabeled samples instead of some screened unlabeled samples. It can eliminate semantic information loss and enhance the model’s resistance to noise from pseudo labels. Our method is evaluated on three public remote sensing datasets, and the experimental results demonstrate its superiority over state-of-the-art (SOTA) semi-supervised methods.