Spatiotemporal Dual Priors Guided Diffusion for Remote Sensing Spatiotemporal Fusion

Remote sensing spatio-temporal fusion (STF) aims to integrate remote sensing images with complementary temporal and spatial resolutions to enhance image quality with high spatio-temporal resolutions. Conditional diffusion models have shown promising performance in STF by learning data probability distribution to achieve accurate fusion results. However, existing conditional diffusion models for STF directly use the limited image exemplars as conditional signals, limiting their ability to extract coherent features across images with extended temporal gaps and significant resolution differences. To address this challenge, this paper presents a spatio-temporal Dual Priors guided Diffusion framework for STF, named DPDiff. The DPDiff has three key modules including a Prior Extraction Network (PENet), a residual diffusion framework (RDF) and a Difference-Aware Module (DAM). First, a lightweight CNN-based PENet is designed to learn the spatio-temporal dual priors as conditional signals to control the diffusion process. The dual priors include a spatial mapping prior (SMP) between the coarse and fine images at the same time, and a temporal change prior (TCP) between the coarse and fine images at different times. The SMP and the TCP are reciprocal to ensure precise control of the diffusion process. Then, an RDF is adapted to learn distribution differences between high temporal resolution and high spatial resolution images, rather than directly reconstructing the entire image. Third, a DAM is introduced to align the CNN-predicted space with the ground truth space. Experimental results on four benchmark datasets — CIA, LGC, WaterBank, and the global E-SMILE — demonstrate that DPDiff achieves superior generation fidelity and quantitative performance across diverse regions and platforms, outperforming state-of-the-art methods. The source code is available at https://github.com/paul623/DPDiff.