CLC²SIE: Cross-Level Consistency Constraint for Semisupervised Building Instance Extraction From High-Resolution Remote Sensing Imagery

Semi-supervised building instance extraction aims to learn from limited labeled data alongside an extensive collection of unlabeled data, offering a promising approach for extracting building instances from high-resolution (HR) remote sensing images (RSIs). However, complex RSIs frequently encounter challenges such as background interference and intricate noise, struggling in generating reliable pseudo labels. To alleviate this, we propose a novel cross-level consistency constraint semi-supervised building instance extraction method (CLC²SIE) to enhance pseudo label generation. Specifically, CLC²SIE contains two core modules: object-level dynamic consistency (OLDC) and pixel-level saliency consistency (PLSC). The OLDC module dynamically converts building features from background into valuable supplementary information, enhancing the model’s perception of building instances in complex scenes. Additionally, the PLSC module is designed to mitigate the boundary noise in pseudo labels by saliency guidance, which improves model’s awareness of building contours. By co-learning these modules in an end-to-end manner, CLC²SIE facilitates pseudo label generation and improves extraction performance. Experiments were conducted on the three public building datasets, i.e., WHU, CrowdAI and TCC, demonstrate that CLC²SIE achieves superior performance compared to state-of-the-art semi-supervised instance extraction methods at different labeling ratios. This study explores a novel semi-supervised learning (SSL) framework that exploits cross-level consistency to improve pseudo label generation, offering a methodological reference for various SSL applications in RSIs.