AdaptMatch: Adaptive Matching for Semisupervised Binary Segmentation of Remote Sensing Images

There are various binary semantic segmentation tasks in remote sensing (RS) that aim to extract the foreground areas of interest, such as buildings and roads, from the background in satellite images. In particular, semi-supervised learning, which can use limited labeled data to guide a large amount of unlabeled data for model training, can significantly promote the fast applications of these tasks in practice. However, due to the predominance of the background in RS images, the foreground only accounts for a small proportion of the pixels. It poses a challenge: models are biased toward the majority class of the background, leading to poor performance on the minority class of the foreground. To address this issue, this paper proposes a novel and effective semi-supervised learning framework, Adaptive Matching (AdaptMatch), for RS binary segmentation. AdaptMatch calculates individual and adaptive thresholds of the foreground and background based on their convergence difficulty in an online manner at the training stage; the adaptive thresholds are then used to select the high-confidence pseudo-labeled data of the two classes for model self-training in turn. Extensive experiments are conducted on two widely-studied RS binary segmentation tasks, building footprint extraction and road extraction, to demonstrate the effectiveness and generalizability of the proposed method. The results show that the proposed AdaptMatch achieves superior performance compared with some state-of-the-art semi-supervised methods in RS binary segmentation tasks. The codes will be publicly available at https://github.com/zhu-xlab/AdaptMatch.