RSDiff: Remote Sensing Image Generation from Text Using Diffusion Model
Ahmad Sebaq Mohamed ElHelw
Satellite imagery generation and super-resolution are pivotal tasks in remote
sensing, demanding high-quality, detailed images for accurate analysis and
decision-making. In this paper, we propose an innovative and lightweight
approach that employs two-stage diffusion models to gradually generate
high-resolution Satellite images purely based on text prompts. Our innovative
pipeline comprises two interconnected diffusion models: a Low-Resolution
Generation Diffusion Model (LR-GDM) that generates low-resolution images from
text and a Super-Resolution Diffusion Model (SRDM) conditionally produced. The
LR-GDM effectively synthesizes low-resolution by (computing the correlations of
the text embedding and the image embedding in a shared latent space), capturing
the essential content and layout of the desired scenes. Subsequently, the SRDM
takes the generated low-resolution image and its corresponding text prompts and
efficiently produces the high-resolution counterparts, infusing fine-grained
spatial details and enhancing visual fidelity. Experiments are conducted on the
commonly used dataset, Remote Sensing Image Captioning Dataset (RSICD). Our
results demonstrate that our approach outperforms existing state-of-the-art
(SoTA) models in generating satellite images with realistic geographical
features, weather conditions, and land structures while achieving remarkable
super-resolution results for increased spatial precision.