Spatio-temporal fusion has become a popular technology for generating remote sensing images with high spatial and high temporal resolutions, thus providing valuable data support for remote sensing monitoring applications, such as environmental monitoring and city planning. Currently, deep learning-based methods have garnered a significant amount of attention, and they mostly employ the fine image at the neighboring date as an auxiliary image. However, capturing usable neighboring fine images may be challenging due to the adverse effects of weather conditions on optical images. Moreover, the fusion performance drops sharply when the temporal interval is long (i.e., there are significant differences in images). In this paper, we proposed a bidirectional pyramid fusion network with semantic prior regularization (BPFN-SPR), which exhibits remarkable flexibility and robustness to temporal intervals.

中文翻译：

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一种基于语义先验正则化的遥感影像时间不敏感时空融合方法


时空融合已成为生成高空间、高时间分辨率遥感影像的热门技术，为环境监测、城市规划等遥感监测应用提供了有价值的数据支持。目前，基于深度学习的方法已经引起了广泛关注，它们大多使用相邻日期的精细图像作为辅助图像。然而，由于天气条件对光学图像的不利影响，捕获可用的相邻精细图像可能具有挑战性。此外，当时间间隔较长时（即图像存在显著差异），融合性能会急剧下降。在本文中，我们提出了一种具有语义先验正则化的双向金字塔融合网络 （BPFN-SPR），它对时间间隔表现出显着的灵活性和鲁棒性。