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Remote Sensing Image Super-Resolution via Residual Aggregation and Split Attentional Fusion Network
2021
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Remote sensing images contain various land surface scenes and different scales of ground objects, which greatly increases the difficulty of super-resolution tasks. The existing deep learning-based methods cannot solve this problem well. To achieve high-quality super-resolution of remote sensing images, a residual aggregation and split attentional fusion network (RASAF) is proposed in this article. It is mainly divided into the following three parts. First, a split attentional fusion block is
doi:10.1109/jstars.2021.3113658
fatcat:pfjvc3kojndndpgvdhhp7vqela