Remote Sensing Image Super-Resolution for Heritage Sites Using a Temporal Invariance-Aware Training Strategy

Effective spatial and structural monitoring of World Heritage sites often relies on continuous high-spatial-resolution remote sensing imagery, which is often unavailable for specific years due to sensor, atmospheric, and revisit constraints. Super-resolution reconstruction thus becomes crucial for maintaining data continuity for such analyses. Traditional methods are trained on temporally aligned LR-HR pairs; however, their performance significantly declines when applied to unseen years due to temporal distribution shifts. To address this, we propose a temporal invariance-aware training strategy combined with an improved Residual Dense Network (RDN_2_M). We introduce a cross-year masked sample generation algorithm that identifies temporally stable regions via local structural similarity. This constructs explicit invariance-guided training pairs, which helps guide the model to focus on persistent structural features rather than transient appearances and to learn robust representations against inter-annual variations. Experiments on the Bin County Cave Temple (BCCT) Heritage Site dataset show our method, integrating the proposed strategy with the enhanced RDN model (RDN_2_M), significantly improves both the objective metrics and visual quality of reconstructed images. This offers a practical solution to filling temporal data gaps, thereby supporting long-term spatial and structural heritage monitoring.