A vision-language model-driven spatiotemporal alignment framework for mitigating cross-sensor inconsistencies in real-world remote sensing image super-resolution

Remote sensing image super-resolution (RSISR) in real-world scenarios is challenged by spectral and geometric inconsistencies across sensors, caused by variations in sensor characteristics and acquisition conditions. These cross-sensor mismatches pose significant challenges for pixel-level supervision, and most existing methods overlook the valuable spatiotemporal metadata embedded in satellite imagery. To overcome these issues, a new metadata-guided framework named Spatiotemporal-Aligned Super-Resolution Framework (SASRF) is developed to incorporate spatiotemporal information into the reconstruction process. At its core, SASRF first leverages a Spatiotemporal Contrastive Learning Module (SCLM) to learn a meaningful alignment between image features and their corresponding spatiotemporal metadata. This learned context representation then guides a Dynamic Feature Alignment Module (DFAM), which performs metadata-aware feature modulation to mitigate spectral and geometric inconsistencies in cross-sensor imagery. The framework is optimized through a two-stage training strategy that decouples cross-modal representation learning from image reconstruction, thereby improving training stability and generalization. Comprehensive evaluations on a large-scale cross-sensor dataset with abundant spatiotemporal metadata verify that SASRF surpasses recent state-of-the-art techniques in reconstruction precision, spectral reliability, and geometric alignment. Moreover, its lightweight and modular design enables seamless integration with various backbone networks, making it a practical solution under real-world RSISR conditions. The code will be available.