LAUSR

Spatio-temporal fusion can generate time-series images with high spatial resolution, and it is highly desirable in various applications, especially in monitoring fine dynamic changes of surface features on remote sensing images. Currently, most spatio-temporal fusion methods predict the target fine image by employing the auxiliary fine images on neighboring phases; however, they are generally limited in abrupt land cover changes between the target and the neighboring auxiliary images. In this article, we propose a novel detail injection-based spatio-temporal fusion (DISTF) model to alleviate this problem, by exploring the inherent relationship between the spatio-temporal fusion and spatio-spectral fusion. The proposed DISTF consists of three modules: a three-branch detail injection (TDI) module, a fine detail prediction (FDP) module, and a reconstruction module. The interpretable TDI module is inspired by spatio-spectral fusion, aiming to inject the non-changed detail information extracted from the neighboring fine images into the target coarse image, which can preserve the abrupt change information captured in the target coarse image. The FDP module is designed to further integrate the correlated information from the outputs of TDI and refine the spatial-spectral information to boost the fusion accuracy. Finally, the reconstruction module and the hybrid loss function are designed to more effective reconstruct the high-quality target fine image. The qualitative and quantitative experimental results on two datasets with different types of changes demonstrated that the proposed DISTF method achieves richer spatial detail and more accurate prediction than the eight existing methods.