A Triple-Branch Model With Style-Unified Contrastive Learning and Adaptive Dice Focal Loss for Change Detection

Deep learning has significantly reshaped the landscape of remote sensing change detection (RSCD). However, detecting subtle changes remains a formidable challenge due to the simultaneous presence of three critical issues: domain shift from seasonal and illumination variations, severe class imbalance between change and background areas, and the need for high deployment efficiency. To address these multifaceted problems in a unified manner, this paper introduces SDTNet, a comprehensive framework featuring three key innovations. First, to counter domain shift, we propose the Style-Unified Temporal Difference Contrastive Learning Strategy (STDCL), a novel, fine-grained contrastive learning method that guides the model to decouple real from pseudo changes without incurring additional inference costs. Second, to mitigate severe class imbalance, we design the Adaptive Dice Focal Loss (ADFLoss), which, unlike static loss functions, introduces two novel adaptive factors to dynamically balance sample weights and implement a curriculum learning strategy. Third, for efficient feature enhancement, we present the Dual-Branch Temporal Difference Transformer (DTDFormer), an efficiency-aware architecture that adapts the differential attention mechanism for bi-temporal inputs and incorporates a sparse attention module to reduce computational complexity. Experimental results demonstrate that our integrated SDTNet framework achieves state-of-the-art (SOTA) performance across five large-scale change detection datasets. The code is publicly available at https://github.com/Baobaon/SDTNet.