Satellite image sequence prediction is a crucial and challenging task. Previous studies leverage optical flow methods or existing deep learning methods on spatial–temporal sequence models for the task. However, they suffer from either oversimplified model assumptions or blurry predictions and sequential error accumulation issue, for a long-term forecast requirement. In this article, we propose a novel multiscale time conditional generative adversarial network (MSTCGAN). To address the sequential error accumulation issue, MSTCGAN adopts a parallel prediction framework to produce the future image sequences by a one-hot time condition input. In addition, a powerful multiscale generator is designed with the multihead axial attention, which helps to carefully preserve the fine-grained details for appearance consistency. Moreover, we develop a temporal discriminator to address the blurry issue and maintain the motion consistency in prediction. Extensive experiments have been conducted on the FengYun-4A satellite dataset, and the results demonstrate the effectiveness and superiority of the proposed method over state-of-the-art approaches.

中文翻译：

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MSTCGAN：用于长期卫星图像序列预测的多尺度时间条件生成对抗网络

卫星图像序列预测是一项至关重要且具有挑战性的任务。先前的研究利用光流方法或现有的关于时空序列模型的深度学习方法来完成任务。然而，对于长期预测要求，它们要么受到过度简化的模型假设或模糊预测和连续误差累积问题的困扰。在本文中，我们提出了一种新颖的多尺度时间条件生成对抗网络（MSTCGAN）。为了解决顺序误差累积问题，MSTCGAN 采用​​并行预测框架，通过 one-hot 时间条件输入生成未来图像序列。此外，还设计了一个强大的多尺度生成器，具有多头轴向注意力，有助于仔细保留细粒度的细节以保持外观一致性。而且，我们开发了一个时间鉴别器来解决模糊问题并保持预测中的运动一致性。在风云四号卫星数据集上进行了广泛的实验，结果证明了所提出的方法相对于最先进的方法的有效性和优越性。