遥感图像时空融合是一种生成兼具高时空分辨率的合成遥感数据的技术。近年来，产生了一些基于卷积神经网络的时空融合方法。这些方法效果良好，但需要较多的图像样本对训练模型，限制了它们的应用。针对此问题，本文提出了一种单样本对卷积神经网络时空融合方法（SS-CNN）。该方法以高空间分辨率图像的波段平均图像提供的空间信息激励卷积神经网络建立高、低空间分辨率图像间的超分关系，进而利用该超分关系映射求解目标高空间分辨率图像。在实验中使用两个模拟数据集和一个真实数据集对该方法进行了测试，并与两种常用的时空融合方法做了比较。实验结果表明，SS-CNN在单样本对训练的情况下，可以较好地预测地物的物候变化和类型的变化，且在异质性高、地块破碎的区域表现良好。其不足之处在于会在地物边界上会造成轻微的模糊，将来需针对此问题做进一步改进。

Spatiotemporal fusion is a feasible way to provide synthetic satellite images with high spatial and high temporal resolution simultaneously. In recent years, some efficient STF methods based on Convolutional Neural Networks (CNNs) have been developed. However, these methods require a significant number of training image pairs, where each pair generally consists of a high spatial resolution image and a low spatial resolution image. Such a requirement limits the applicability of STF methods to actual scenarios because image pairs for training are not widely available in many cases. To overcome this important limitation, we introduce a CNN-based single image pair method for STF of remotely sensed images. Our method, called SS-CNN, uses the spatial information provided by the average image (obtained across available spectral bands) of the high spatial resolution image to perform CNN-based Super-Resolution Mapping (SRM) between the low and high spatial resolution images. The proposed SS-CNN has been tested in experiments using two simulated and one real dataset and compared with two commonly used spatiotemporal fusion methods. The experimental results show that SS-CNN can predict the phenological changes and land cover changes well. Plus, its performance in heterogeneous areas is remarkable. The disadvantage is that it will slightly blur the boundary, which needs to be further improved in the future.