  Remote sensing images captured by different platforms exhibit significant
disparities in spatial resolution. Large scale factor super-resolution (SR)
algorithms are vital for maximizing the utilization of low-resolution (LR)
satellite data captured from orbit. However, existing methods confront
challenges in recovering SR images with clear textures and correct ground
objects. We introduce a novel framework, the Semantic Guided Diffusion Model
(SGDM), designed for large scale factor remote sensing image super-resolution.
The framework exploits a pre-trained generative model as a prior to generate
perceptually plausible SR images. We further enhance the reconstruction by
incorporating vector maps, which carry structural and semantic cues. Moreover,
pixel-level inconsistencies in paired remote sensing images, stemming from
sensor-specific imaging characteristics, may hinder the convergence of the
model and diversity in generated results. To address this problem, we propose
to extract the sensor-specific imaging characteristics and model the
distribution of them, allowing diverse SR images generation based on imaging
characteristics provided by reference images or sampled from the imaging
characteristic probability distributions. To validate and evaluate our
approach, we create the Cross-Modal Super-Resolution Dataset (CMSRD).
Qualitative and quantitative experiments on CMSRD showcase the superiority and
broad applicability of our method. Experimental results on downstream vision
tasks also demonstrate the utilitarian of the generated SR images. The dataset
and code will be publicly available at https://github.com/wwangcece/SGDM
