Disentangling Semi-Supervised Semantic Segmentation of Remote Sensing Images

In Earth observation, semantic understanding of Remote Sensing (RS) images holds significant importance, yet it is hindered in practice by the need for extensive manual pixel-level labeling. Semi-supervised semantic segmentation (SSS) of RS images would be a promising solution, which fully utilizes unlabeled data for model self-training under the guidance of limited labeled data. The mainstream SSS methods use pseudo-labels of the unlabeled data for model training, however, their performance is bottlenecked because of confirmation bias, i.e., stubborn incorrect pseudo-labels. To counter this, our study introduces a novel disentanglement learning (DL) method tailored for RS-SSS. It separates the predictions of the labeled and unlabeled data by two individual prediction heads during current training, and then integrates them during follow-up training. The experimental results verify its effectiveness on two widely-used RS semantic segmentation datasets in semi-supervised setting.