An Enhanced Deep Convolutional Model for Spatiotemporal Image Fusion
An Enhanced Deep Convolutional Model for Spatiotemporal Image Fusion
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DOI：
10.3390/rs11242898
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发表时间：
2019-12
期刊：
影响因子：
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通讯作者：
Zhenyu Tan;L. Di;Mingda Zhang;Liying Guo;Meiling Gao
中科院分区：
文献类型：
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作者：
Zhenyu Tan;L. Di;Mingda Zhang;Liying Guo;Meiling Gao
Earth observation data with high spatiotemporal resolution are critical for dynamic monitoring and prediction in geoscience applications, however, due to some technique and budget limitations, it is not easy to acquire satellite images with both high spatial and high temporal resolutions. Spatiotemporal image fusion techniques provide a feasible and economical solution for generating dense-time data with high spatial resolution, pushing the limits of current satellite observation systems. Among existing various fusion algorithms, deep-learning-based models reveal a promising prospect with higher accuracy and robustness. This paper refined and improved the existing deep convolutional spatiotemporal fusion network (DCSTFN) to further boost model prediction accuracy and enhance image quality. The contributions of this paper are twofold. First, the fusion result is improved considerably with brand-new network architecture and a novel compound loss function. Experiments conducted in two different areas demonstrate these improvements by comparing them with existing algorithms. The enhanced DCSTFN model shows superior performance with higher accuracy, vision quality, and robustness. Second, the advantages and disadvantages of existing deep-learning-based spatiotemporal fusion models are comparatively discussed and a network design guide for spatiotemporal fusion is provided as a reference for future research. Those comparisons and guidelines are summarized based on numbers of actual experiments and have promising potentials to be applied for other image sources with customized spatiotemporal fusion networks.
