Artificial Intelligence Generative Content (AIGC) technologies have significantly influenced the remote sensing domain, particularly in the realm of image generation. However, remote sensing image editing, an equally vital research area, has not garnered sufficient attention. Different from text-guided editing in natural images, which relies on extensive text-image paired data for semantic correlation, the application scenarios of remote sensing image editing are often extreme, such as forest on fire, so it is difficult to obtain sufficient paired samples. At the same time, the lack of remote sensing semantics and the ambiguity of text also restrict the further application of image editing in remote sensing field. To solve above problems, this letter proposes a diffusion based method to fulfill stable and controllable remote sensing image editing with text guidance. Our method avoids the use of a large number of paired image, and can achieve good image editing results using only a single image. The quantitative evaluation system including CLIP score and subjective evaluation metrics shows that our method has better editing effect on remote sensing images than the existing image editing model.

翻译：人工智能生成内容技术显著影响了遥感领域，尤其在图像生成方面。然而，遥感图像编辑这一同样重要的研究方向却未受到足够重视。与依赖大量文本-图像配对数据建立语义关联的自然图像文本引导编辑不同，遥感图像编辑的应用场景往往较为极端（如森林火灾），导致难以获取充足的配对样本。同时，遥感语义的缺失及文本的歧义性问题也制约了图像编辑在遥感领域的进一步应用。为解决上述问题，本文提出了一种基于扩散的方法，实现文本引导下稳定可控的遥感图像编辑。该方法无需大量配对图像，仅需单张图像即可获得良好的编辑效果。包含CLIP评分与主观评价指标的量化评估体系表明，本方法在遥感图像上的编辑效果优于现有图像编辑模型。