Abstract
We introduce an innovative deep learning-based method that uses a denoising diffusion-based model to translate low-resolution images to high-resolution ones from different optical sensors while preserving the contents and avoiding undesired artifacts. The proposed method is trained and tested on a large and diverse data set of paired Sentinel-II and Planet Dove images. We show that it can solve serious image generation issues observed when the popular classifier-free guided Denoising Diffusion Implicit Model (DDIM) framework is used in the task of Image-to-Image Translation of multi-sensor optical remote sensing images and that it can generate large images with highly consistent patches, both in colors and in features. Moreover, we demonstrate how our method improves heterogeneous change detection results in two urban areas: Beirut, Lebanon, and Austin, USA. Our contributions are: i) a new training and testing algorithm based on denoising diffusion models for optical image translation; ii) a comprehensive image quality evaluation and ablation study; iii) a comparison with the classifier-free guided DDIM framework; and iv) change detection experiments on heterogeneous data.
Abstract (translated)
我们提出了一个基于去噪扩散模型的创新深度学习方法，用于从不同光学传感器将低分辨率图像翻译为高分辨率图像，同时保留内容并避免不必要的伪影。所提出的方法在大量和多样化的数据集上进行训练和测试，对成对的Sentinel-II和Planet Dove图像进行图像到图像翻译。我们证明了它可以解决当使用流行的无指导去噪扩散隐式模型（DDIM）框架在多传感器光学遥感图像图像到图像转换任务中观察到的严重图像生成问题，并且它可以生成具有高度一致性的大图像，无论是颜色还是特征。此外，我们还证明了我们的方法在两个城市地区改善了异质变化检测结果：黎巴嫩的贝鲁特和美国的奥斯汀。我们的贡献是：i）基于去噪扩散模型的新的训练和测试算法；ii）全面图像质量评估和消融研究；iii）与无分类指导的DDIM框架的比较；和iv）在异质数据上的变化检测实验。
URL
https://arxiv.org/abs/2404.11243
