  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.
