RDF-MIG：一种用于掩码图像生成的鲁棒扩散框架，以增强语义分割和变化检测
RDF-MIG: A Robust Diffusion Framework for Masked Image Generation to Augment Semantic Segmentation and Change Detection
提出一种鲁棒的扩散框架，通过生成双时相变化标注和单时相分割标注的多光谱图像-掩码对来增强下游变化检测和语义分割任务；其MCRD损失和MSE一致性校准可缓解噪声累积问题，可复用于其他需要生成高质量图像-掩码对的视觉任务。
原文依据 RDF-MIG generates bi-temporal change-labeled and single-temporal segmentation-labeled images to enhance downstream change detection and semantic segmentation tasks.
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论变化检测和语义分割是遥感卫星图像分析中的关键技术。然而，获取高质量的标注数据既昂贵又耗时。尽管近期研究已探索利用生成模型来缓解数据稀缺问题，但仍缺乏一个支持这两种任务的统一框架，且大多数方法忽略了噪声累积问题，也无法生成多光谱图像。为此，我们提出了用于掩码图像生成的鲁棒扩散框架（RDF-MIG）。RDF-MIG生成双时相变化标注和单时相分割标注的图像，以增强下游的变化检测和语义分割任务。此外，为解决噪声累积问题并提升生成的图像-掩码对的质量，我们通过提出最大相关熵鲁棒扩散（MCRD）损失重新构建了扩散模型的训练目标，并进一步设计了一种MSE一致性校准方法，该方法在保持对异常值鲁棒性的同时，以解析方式将小误差梯度与MSE目标对齐。实验表明，所提出的RDF-MIG框架能够生成多光谱图像-掩码对以提升下游性能，而MCRD损失进一步提高了合成数据的质量。
英文原文摘要 1259 字符
Change detection and semantic segmentation are key techniques for satellite image analysis in remote sensing. However, acquiring high-quality labeled data is costly and time-consuming. Although recent studies have explored generative models to ease data scarcity, a unified framework supporting both tasks is still lacking, and most methods overlook noise accumulation and cannot generate multispectral images. To address this, we propose the robust diffusion framework for masked image generation (RDF-MIG). RDF-MIG generates bi-temporal change-labeled and single-temporal segmentation-labeled images to enhance downstream change detection and semantic segmentation tasks. Furthermore, to address noise accumulation and improve the quality of generated image-mask pairs, we reformulate the diffusion model training objective by proposing the Maximum Correntropy Robust Diffusion (MCRD) loss, and further design an MSE-consistency calibration that analytically aligns small-error gradients with the MSE objective while preserving robustness to outliers. Experiments indicate that the proposed RDF-MIG framework can generate multispectral image-mask pairs to improve downstream performance, while MCRD loss further enhances the quality of the synthesized data.