RSDiff：基于扩散模型的文本生成遥感图像
RSDiff: Remote Sensing Image Generation from Text Using Diffusion Model
AI总结：
本研究针对遥感领域卫星图像生成增强需求，提出两阶段扩散模型RSDiff，通过LRDM生成初始图、SRDM优化分辨率，在RSICD数据集上表现优于现有模型，可生成地理细节准确的高分辨率卫星图像。
摘要：
卫星图像的生成与增强是遥感领域的关键任务，需要高质量、细节丰富的图像以支撑精准分析。本研究提出一种两阶段扩散模型方法，可根据文本提示合成高分辨率卫星图像。该流程包含低分辨率扩散模型（LRDM）和超分辨率扩散模型（SRDM），前者根据文本输入生成初始图像，后者将这些图像优化为高分辨率输出。LRDM在共享潜空间中融合文本与图像嵌入，捕捉核心场景内容与结构；SRDM则对图像进行增强，重点提升空间特征与视觉清晰度。在遥感图像描述数据集（RSICD）上开展的实验表明，本方法性能优于现有模型，生成的卫星图像地理细节准确，空间分辨率有所提升。
英文摘要：
The generation and enhancement of satellite imagery are critical in remote sensing, requiring high-quality, detailed images for accurate analysis. This research introduces a two-stage diffusion model methodology for synthesizing high-resolution satellite images from textual prompts. The pipeline comprises a Low-Resolution Diffusion Model (LRDM) that generates initial images based on text inputs and a Super-Resolution Diffusion Model (SRDM) that refines these images into high-resolution outputs. The LRDM merges text and image embeddings within a shared latent space, capturing essential scene content and structure. The SRDM then enhances these images, focusing on spatial features and visual clarity. Experiments conducted using the Remote Sensing Image Captioning Dataset (RSICD) demonstrate that our method outperforms existing models, producing satellite images with accurate geographical details and improved spatial resolution.
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