The letter investigates the utility of text-to-image inpainting models for satellite image data. Two technical challenges of injecting structural guiding signals into the generative process as well as translating the inpainted RGB pixels to a wider set of MSI bands are addressed by introducing a novel inpainting framework based on StableDiffusion and ControlNet as well as a novel method for RGB-to-MSI translation. The results on a wider set of data suggest that the inpainting synthesized via StableDiffusion suffers from undesired artifacts and that a simple alternative of self-supervised internal inpainting achieves a higher quality of synthesis.

翻译：本文探究了文本到图像修复模型在卫星图像数据中的应用潜力。针对生成过程中注入结构引导信号以及将修复后的RGB像素转换为更广泛的多光谱波段（MSI）这两项技术挑战，提出了一种基于StableDiffusion和ControlNet的新型修复框架，以及一种RGB到MSI转换的新方法。在更广泛数据集上的结果表明，通过StableDiffusion合成的修复图像存在不期望的伪影，而一种简单的自监督内部修复替代方案能够实现更高质量的合成效果。