The paper 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 artefacts and that a simple alternative of self-supervised internal inpainting achieves higher quality of synthesis.

研究了文本到图像修复模型在卫星图像数据中的实用性，通过引入基于StableDiffusion和ControlNet的新型修复框架以及一种新的RGB到MSI转换方法，解决了将结构引导信号注入到生成过程中的两个技术挑战，以及将修复的RGB像素转换为更广泛的MSI波段。对更广泛的数据集的结果表明，通过StableDiffusion生成的修复结果存在不受欢迎的伪影，而自监督内部修复的简单替代方法具有更高的合成质量。

利用结构边缘引导探索文本到图像扩散模型在多光谱卫星图像修复中的能力