Due to hardware technology limitations, satellite sensors are unable to capture images with high temporal, spatial, and spectral resolutions simultaneously. However, the Gaofen-1 satellite overcomes this challenge by incorporating 2-meter panchromatic, 8-meter multispectral, and 16-meter wide-field cameras, allowing for the integration of images from these sensors. To address this issue, we propose a study on the spatio-temporal-spectral fusion method for Gaofen-1 images, aiming to achieve more comprehensive structures. Inspired by the diffusion model, which learns the data distribution of the target image, we propose a new network utilizing an enhanced diffusion framework. The network incorporates both structural and spectral constraints to guide the fusion process. This work represents the first application of the diffusion model to spatio-temporal-spectral fusion, specifically synthesizing the 2-meter multispectral images with dense temporal resolution. To assess fusion quality, we have developed a benchmark dataset. During the validation stage, we evaluate the radiometric deviation, structural similarity, and spectral fidelity between the fused 2-meter multispectral images and the reference images. Both visual and quantitative assessments demonstrate that our newly proposed method work well for the Gaofen-1 fusion.

中文翻译：

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高分一号同质卫星平台时空谱融合扩散模型

由于硬件技术的限制，卫星传感器无法同时捕获高时间、空间和光谱分辨率的图像。然而，高分一号卫星通过整合2米全色、8米多光谱和16米宽视场相机克服了这一挑战，允许整合来自这些传感器的图像。为了解决这个问题，我们提出了对高分一号图像的时空谱融合方法的研究，旨在获得更全面的结构。受扩散模型（学习目标图像的数据分布）的启发，我们提出了一种利用增强扩散框架的新网络。该网络结合了结构和光谱约束来指导融合过程。这项工作代表了扩散模型在时空光谱融合中的首次应用，特别是合成了具有密集时间分辨率的 2 米多光谱图像。为了评估融合质量，我们开发了一个基准数据集。在验证阶段，我们评估融合的 2 米多光谱图像与参考图像之间的辐射偏差、结构相似性和光谱保真度。视觉和定量评估都表明我们新提出的方法对于 Gaufen-1 融合效果很好。