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Mixed 2D/3D Convolutional Network for Hyperspectral Image Super-Resolution
2020
Remote Sensing
Deep learning-based hyperspectral image super-resolution (SR) methods have achieved great success recently. However, there are two main problems in the previous works. One is to use the typical three-dimensional convolution analysis, resulting in more parameters of the network. The other is not to pay more attention to the mining of hyperspectral image spatial information, when the spectral information can be extracted. To address these issues, in this paper, we propose a mixed convolutional
doi:10.3390/rs12101660
fatcat:dwbplbfdc5hbjfpmk32iltwg4i