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Improving Autoencoder Training Performance for Hyperspectral Unmixing with Network Reinitialisation
Neural networks, in particular autoencoders, are one of the most promising solutions for unmixing hyperspectral data, i. e. reconstructing the spectra of observed substances (endmembers) and their relative mixing fractions (abundances), which is needed for effective hyperspectral analysis and classification.
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