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.

翻译：扩散模型的出现彻底改变了图像生成领域，为各类应用创建高质量、高分辨率图像提供了新方法。然而，这些模型在生成特定领域图像（尤其是遥感图像）方面的潜力仍未得到充分挖掘。遥感图像以其高分辨率、广阔覆盖范围和丰富信息内容而著称，这带来了通用扩散模型可能无法充分应对的新挑战。本文提出CRS-Diff，这是一种专为生成遥感影像而设计的开创性扩散建模框架，它充分利用扩散模型的固有优势，同时集成先进的控制机制，确保生成的图像不仅视觉清晰，还富含地理和时间信息。该模型整合了全局和局部控制输入，能够实现生成条件的精确组合以优化生成过程。对CRS-Diff的综合评估表明，在单条件和多条件生成场景下，该模型在图像质量和多样性方面均优于现有方法。