The emergence of diffusion models has revolutionized the field of image
generation, providing new methods for creating high-quality, high-resolution
images across various applications. However, the potential of these models for
generating domain-specific images, particularly remote sensing (RS) images,
remains largely untapped. RS images that are notable for their high resolution,
extensive coverage, and rich information content, bring new challenges that
general diffusion models may not adequately address. This paper proposes
CRS-Diff, a pioneering diffusion modeling framework specifically tailored for
generating remote sensing imagery, leveraging the inherent advantages of
diffusion models while integrating advanced control mechanisms to ensure that
the imagery is not only visually clear but also enriched with geographic and
temporal information. The model integrates global and local control inputs,
enabling precise combinations of generation conditions to refine the generation
process. A comprehensive evaluation of CRS-Diff has demonstrated its superior
capability to generate RS imagery both in a single condition and multiple
conditions compared with previous methods in terms of image quality and
diversity.

提出了一种专门针对遥感图像生成的扩散建模框架 CRS-Diff，利用扩散模型的内在优势并整合先进的控制机制，以确保图像不仅在视觉上清晰，而且充满地理和时间信息。综合评估证明，CRS-Diff 在图像质量和多样性方面相对于之前的方法具有卓越的生成能力。