An unsupervised hyperspectral image super-resolution technique trains a neural network on fully synthetic abundance maps that mimic real statistics via the dead leaves model, then reconstructs the high-resolution image from upsampled abundances and endmembers.
Evaluation of aerial remote sensing techniques for vegetation management in power-line cor- ridors,
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Unsupervised Super-Resolution of Hyperspectral Remote Sensing Images Using Fully Synthetic Training
An unsupervised hyperspectral image super-resolution technique trains a neural network on fully synthetic abundance maps that mimic real statistics via the dead leaves model, then reconstructs the high-resolution image from upsampled abundances and endmembers.