Nowadays, data is of paramount importance for artificial intelligence. However, collecting real-world hyperspectral images (HSIs) with desired characteristics and diversity can be prohibitively expensive and time-consuming, leading to the data scarcity issue in HSI, and further limiting the potential of deep learning-based HSI applications. Existing work to tackle this issue fails to generate abundant, diverse, and reliable synthetic HSIs. This work proposes a multi-modal scene fusion-based method that diffusion from the abundance perspective for HSI synthesis, termed MSF-Diff. Concretely, highlights involve: (1) Synthesis in low-dimensional abundance space, other than original high-dimensional HSI space, greatly releases the difficulty; (2) Integration of multi-modal data greatly enriches the diversity of spatial distribution that the model can perceive; (3) Incorporation of the unmixing concept ensures that the generated synthetic HSI has reliable spectral profiles. The proposed research can generate a vast amount of HSI with a rich diversity in various categories and scenes, closely resembling realistic data. It plays a pivotal role in ensuring that the model produces reliable results and can be trusted for real-world applications. The code is publicly available at .

中文翻译：

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从丰度角度：基于多模态场景融合的高光谱图像合成

如今，数据对于人工智能来说至关重要。然而，收集具有所需特征和多样性的真实世界高光谱图像 (HSI) 可能非常昂贵且耗时，导致 HSI 中的数据稀缺问题，并进一步限制基于深度学习的 HSI 应用的潜力。解决这一问题的现有工作未能产生丰富、多样化且可靠的合成 HSI。这项工作提出了一种基于多模态场景融合的方法，从丰度角度进行扩散以进行 HSI 合成，称为 MSF-Diff。具体来说，亮点包括：（1）在低维丰度空间中合成，而不是在原来的高维HSI空间中，大大释放了难度； （2）多模态数据的融合极大丰富了模型可感知的空间分布的多样性； (3) 分解概念的结合确保生成的合成 HSI 具有可靠的光谱轮廓。所提出的研究可以生成大量具有各种类别和场景的丰富多样性的HSI，与现实数据非常相似。它在确保模型产生可靠的结果并在实际应用中值得信赖方面发挥着关键作用。该代码可在 公开获取。