Object-Level Contrastive-Learning-Based Multi-Branch Network for Building Change Detection from Bi-Temporal Remote Sensing Images

Buildings are fundamental elements of human environments, and detecting changes in them is crucial for land cover studies, urban expansion monitoring, and the detection of illegal construction activities. Existing methods primarily focus on pixel-level differences in bi-temporal remote sensing imagery. However, pseudo-changes, such as variations in non-building areas caused by differences in illumination, seasonal changes, and other factors, pose significant challenges for reliable building change detection. To address these issues, we propose a novel object-level contrastive-learning-based multi-branch network (OCL-Net) for detecting building changes by integrating bi-temporal remote sensing images. First, we design a multi-head decoder to separately extract more distinguishable building change features and auxiliary semantic features from bi-temporal images, effectively leveraging building-specific priors. Second, an object-level contrastive learning loss is designed and jointly optimized with a pixel-level similarity loss to ensure the global consistency of buildings. Finally, an attention-based discriminative feature generation and fusion block is designed to enhance the representation of multi-scale change features. We validate the effectiveness of the proposed method through comparative experiments on the publicly available WHU-CD and S2Looking datasets. Our approach achieves IoU values of 88.54% and 51.94%, respectively, surpassing state-of-the-art methods for building change detection.