PairTrans: Real-Pair-Grounded Semantic-Visual Transformation for Semantic Change Detection

Semantic change detection (SCD) learns land-cover semantics and temporal transitions from densely annotated bi-temporal image-mask pairs. Such paired annotations are expensive to acquire. Existing generative augmentation methods construct additional change data from prompts, simulation rules, noise manipulation, or single-temporal states. Although effective, they do not explicitly construct new supervision by conditioning each synthesized temporal side on the opposite observation of the same annotated pair. We formulate SCD augmentation as bidirectional source-conditioned target-side generation from real annotated pairs and propose PairTrans. For target semantics, PairTrans derives a class-to-class transition matrix from the paired masks and perturbs it to propose new states with structured class transitions. For target appearance, it realizes each generated state from both its mask and the opposite-time real image. Applying the two directed generators symmetrically produces a fully synthetic pair whose semantic transitions are pair-guided and whose visual realizations are conditioned on observed scene context. Across three datasets and five SCD architectures, PairTrans achieves strong gains on SECOND and Landsat in low-data regimes, improves change-region metrics on HiUCD, and remains competitive as real supervision increases.