Spatiotemporal fusion aims to reconstruct sequence remote sensing images in an economically efficient way, for which we observe that the sensor and scale errors can approach the distribution of Gaussian noise. To model the random noise, a spatiotemporal fusion method based on a conditional diffusion model is proposed. A new encoder–decoder network is designed to fuse multisource images. The new model learns the noise distribution at the forward diffusion stage and employs an iterative removal of the noise at the backward diffusion stage, which enhances the model against the Gaussian noise. The proposed method is evaluated on two datasets and compared with seven state-of-the-art algorithms, in which the average root mean square errors (RMSEs) decrease from 0.0198 to 0.0188 for Landsat-7 and from 0.0155 to 0.0141 for Landsat-5, respectively. The experimental results also demonstrate that the proposed method can preserve clearer details and adapt better to abrupt phenological changes.

中文翻译：

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通过条件扩散模型进行时空融合

时空融合旨在以经济有效的方式重建序列遥感图像，为此我们观察到传感器和尺度误差可以接近高斯噪声的分布。为了对随机噪声进行建模，提出了一种基于条件扩散模型的时空融合方法。设计了一种新的编码器-解码器网络来融合多源图像。新模型在前向扩散阶段学习噪声分布，并在后向扩散阶段采用迭代去除噪声，从而增强了模型的抗高斯噪声能力。该方法在两个数据集上进行了评估，并与七种最先进的算法进行了比较，其中 Landsat-7 的平均均方根误差 (RMSE) 从 0.0198 减少到 0.0188，Landsat-5 从 0.0155 减少到 0.0141 ， 分别。实验结果还表明，该方法可以保留更清晰的细节并更好地适应突然的物候变化。