Abstract. Deep learning has significantly improved the accuracy of remote sensing semantic segmentation, yet its effectiveness is often constrained by the limited availability of annotated training samples. Semi-supervised learning (SSL) addresses this challenge by utilizing abundant unlabeled data, reducing dependence on manual annotations. However, current consistency regularization-based SSL methods, primarily developed for natural images, struggle to produce adequate perturbation diversity for robust model training in remote sensing image segmentation. In this work, we propose FusionMatch, a novel SSL framework featuring two perturbation mechanisms - NIRPerb and PSPerb - specifically designed for remote sensing imagery. NIRPerb utilizes near-infrared spectral data to enhance perturbation diversity. PSPerb adopts differentiated pan-sharpening fusion strategies to expand the perturbation space. Extensive experiments on both a building extraction dataset and a multi-class dataset demonstrate that FusionMatch outperforms state-of-the-art SSL methods in segmentation accuracy and robustness.
Zhou et al. (Wed,) studied this question.