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- W4313441892 abstract "Due to the dramatic scale changes in different ground object characteristics, effectively utilizing multi-scale features of remote sensing images represents a major challenge. This letter proposes a sub-pixel convolution-based improved bidirectional feature pyramid network (SCIBFPN) to address the problem. To reduce the information loss in the process of image pre-processing and multi-scale feature fusion, this letter introduces sub-pixel convolution instead of up-sampling. To obtain richer spatial and spectral information, an improved bidirectional feature pyramid network-based sub-pixel convolution is combined with a residual network (ResNet) for feature extraction. In addition, to further enhance the spatial structure of the fused images, a panchromatic (PAN) image is used as a guide to direct the injection of spatial information. Experimental results from two real datasets show that the proposed method outperforms state-of-the-art methods in terms of both objective metrics and subjective visual evaluation." @default.
- W4313441892 created "2023-01-06" @default.
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- W4313441892 date "2023-01-01" @default.
- W4313441892 modified "2023-10-18" @default.
- W4313441892 title "A sub-pixel convolution-based improved bidirectional feature pyramid network for pansharpening" @default.
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- W4313441892 doi "https://doi.org/10.1080/2150704x.2022.2162350" @default.
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