LGTC: Local-Global Tri-Consistency Network for Semi-Supervised Change Detection of Remote Sensing Images

Recently, semi-supervised change detection (SSCD) has attracted considerable attention due to its remarkable capability to enhance model performance with limited annotated data. However, most existing SSCD methods rely primarily on pixel-level consistency learning to leverage unlabeled data. Although this approach can provide basic prediction consistency constraints, it exhibits notable limitations in capturing spatial continuity and global semantic coherence in structural change regions, and is easily affected by noise. To address this issue, we propose a novel SSCD framework, termed the local-global tri-consistency network (LGTC). LGTC incorporates a tri-consistency learning strategy at the pixel, region, and image levels, which provides complementary supervisory signals from fine-grained to global semantic scales, significantly enhancing representation ability and robustness on unlabeled data. Furthermore, a local-global interaction block (LGIBlock) is introduced to integrate local feature details with global context. Within it, the GFTBlock employs frequency domain attention to achieve efficient modeling of global information while effectively suppressing background noise, further enhancing the recognition performance of the model. Experimental results demonstrate that LGTC achieves state-of-the-art performance, attaining F1 scores of 87.12%, 88.65% and 83.25% on the LEVIR-CD, WHU-CD and CDD-CD dataset with 5% labeled data, respectively, fully verifying the effectiveness of the proposed method. Our source codes are available at https://github.com/sherryxu21/LGTC.