A novel end-to-end dropout concrete autoencoder for band selection on hyperspectral images outperforms prior methods on four scenes by training directly on the target band subset.
Estimation of Mineral Abundance from Hyperspectral Data Using a New Supervised Neighbor-Band Ratio Un- mixing Approach,
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Dropout Concrete Autoencoder for Band Selection on HSI Scenes
A novel end-to-end dropout concrete autoencoder for band selection on hyperspectral images outperforms prior methods on four scenes by training directly on the target band subset.