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CRS-Diff: Controllable Remote Sensing Image Generation With Diffusion Model
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AI
DOI：10.1109/TGRS.2024.3453414.png)
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Abstract
En 中文
The emergence of generative models has revolutionized the field of remote sensing (RS) image generation. Despite generating high-quality images, existing methods are limited in relying mainly on text control conditions, and thus do not always generate images accurately and stably. In this article, we propose CRS-Diff, a new RS generative framework specifically tailored for RS image generation, leveraging the inherent advantages of diffusion models while integrating more advanced control mechanisms. Specifically, CRS-Diff can simultaneously support text-condition, metadata-condition, and image-condition control inputs, thus enabling more precise control to refine the generation process. To effectively integrate multiple condition control information, we introduce a new conditional control mechanism to achieve multiscale feature fusion (FF), thus enhancing the guiding effect of control conditions. To the best of our knowledge, CRS-Diff is the first multiple-condition controllable RS generative model. Experimental results in single-condition and multiple-condition cases have demonstrated the superior ability of our CRS-Diff to generate RS images both quantitatively and qualitatively compared with previous methods. Additionally, our CRS-Diff can serve as a data engine that generates high-quality training data for downstream tasks, e.g., road extraction. The code is available at https://github.com/Sonettoo/CRS-Diff.
Keywords:
Diffusion models
Image synthesis
Image resolution
Text to image
Remote sensing
Training
Task analysis
Controllable generation
deep learning
diffusion model
remote sensing (RS) image
Journal
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8.6
Papers:
2.1W
Citations:
10.7W
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