Advancing Data-Efficient Exploitation for Semi-Supervised Remote Sensing Images Semantic Segmentation

To reduce the dependence of remote sensing (RS) image semantic segmentation models on extensive pixel-level annotated images, this article aims to address the issue of insufficient exploitation of RS images' potential within existing semi-supervised learning methods, introducing a novel semi-supervised RS image semantic segmentation method. Specifically, for unlabeled samples, the multiperturbation dynamic consistency (MDC) is proposed to align multiple predictions from diverse data augmentations; MDC leverages a dynamic decay threshold (DDT) instead of fixed thresholds to learn more reliable information, enriching the perturbation space and assisting the segmentation model in acquiring more discriminative feature representations. Furthermore, considering the rich contextual information in RS images, the class prototype memory (CPM) derived from labeled samples is maintained during the training stage, which is leveraged to guide the refinement of predictions from segmentation model at the inference stage. Extensive experiments are conducted on six RS image semantic segmentation datasets, including DFC22, iSAID, MER, MSL, GID-15, and Vaihingen. The experimental results demonstrate the superiority of the proposed method. The code is available at https://github.com/lvliang6879/MCSS.