SatSynth：通过扩散模型增强图像-掩码对以用于航空语义分割
SatSynth: Augmenting Image-Mask Pairs through Diffusion Models for Aerial Semantic Segmentation
AI总结：
本研究提出SatSynth方法，利用去噪扩散概率模型学习图像与标签的联合数据流形，首次为卫星分割同时生成高质量图像和掩码对作为数据增强，显著提升了航空语义分割性能。
摘要：
近年来，语义分割已成为处理和解释卫星图像的关键工具。然而，监督学习技术的一个普遍局限在于需要专家进行大量手动标注。在本研究中，我们探索了生成式图像扩散在解决地球观测任务中标注数据稀缺问题方面的潜力。主要思想是利用去噪扩散概率模型的最新进展，学习图像和标签的联合数据流形。据我们所知，我们是首个为卫星分割同时生成图像和对应掩码的研究。我们发现，获得的图像对不仅在细粒度特征上展现出高质量，还确保了广泛的采样多样性。这两个方面对于地球观测数据至关重要，因为其语义类别在尺度和出现频率上可能存在巨大差异。我们将这些新颖的数据实例用于下游分割任务，作为一种数据增强形式。在我们的实验中，我们与基于判别式扩散模型或GANs的先前工作进行了比较。我们证明，整合生成的样本为卫星语义分割带来了显著的定量改进——无论是与基线相比，还是在仅使用原始数据进行训练时。
英文摘要：
In recent years, semantic segmentation has become a pivotal tool in processing and interpreting satellite imagery. Yet, a prevalent limitation of supervised learning techniques remains the need for extensive manual annotations by experts. In this work, we explore the potential of generative image diffusion to address the scarcity of annotated data in earth observation tasks. The main idea is to learn the joint data manifold of images and labels, leveraging recent advancements in denoising diffusion probabilistic models. To the best of our knowledge, we are the first to generate both images and corresponding masks for satellite segmentation. We find that the obtained pairs not only display high quality in fine-scale features but also ensure a wide sampling diversity. Both aspects are crucial for earth observation data, where semantic classes can vary severely in scale and occurrence frequency. We employ the novel data instances for downstream segmentation, as a form of data augmentation. In our experiments, we provide comparisons to prior works based on discriminative diffusion models or GANs. We demonstrate that integrating generated samples yields significant quantitative improvements for satellite semantic segmentation -- both compared to baselines and when training only on the original data.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
