CEBlock: Balance Factor-Driven Change Enhancement Block via Supervised Contrastive Learning for Remote Sensing Change Detection

The scarcity of annotated datasets remains a primary obstacle for remote sensing change detection (RSCD). Most current methods address this by designing entirely new model architectures from scratch, a process that is labor-intensive and often yields only marginal performance gains. While supervised contrastive learning can enhance feature discrimination with limited labels, it suffers from two critical drawbacks in the RSCD domain: First, the computation of fine-grained pixel-wise contrastive loss on lengthy pixel sequences imposes a heavy memory burden. Second, the inherent coupling of gradient contributions from positive and negative sample pairs in changed regions weakens the discrimination of true changes. To overcome these limitations, we propose a Balance Factor-Driven Change Enhancement Block (CEBlock). Our solution incorporates a Two-Stage Feature Compression (TSFC) mechanism to efficiently compress lengthy bitemporal sequences, reducing memory requirements and enabling more precise identification of changes. Additionally, we introduce a piecewise loss function that uses a balance factor to replace the correlation calculation of positive and negative samples in real change regions, so as to separate the gradient contributions of positive and negative sample pairs in real change regions, thereby enhancing the model’s attention to change features. A key advantage of the CEBlock is its plug-and-play design; it can be directly integrated into the siamese feature extraction networks of existing models (including CNN, Transformer, and Mamba frameworks) to optimize feature alignment with minimal effort. Experiments show that methods augmented with our CEBlock achieve comprehensive performance enhancements on WHU-CD, DSIFN-CD and SYSU-CD datasets, and establish new state-of-the-art results on two public benchmarks, with F1/IoU improvements of 0.51%/0.77% and 3.30%/3.68% on SYSU-CD and DSIFN-CD, respectively.