  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. However, as we show in this paper, the training of autoencoders
for unmixing is highly dependent on weights initialisation; some sets of
weights lead to degenerate or low-performance solutions, introducing negative
bias in the expected performance. In this work, we experimentally investigate
autoencoders stability as well as network reinitialisation methods based on
coefficients of neurons' dead activations. We demonstrate that the proposed
techniques have a positive effect on autoencoder training in terms of
reconstruction, abundances and endmembers errors.
