Unmixing in the presence of nuisances with deep generative models

IGARSS, pp. 5189-5192, 2017.

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Abstract:

Spectral datasets acquired for unmixing are noisy and largely unlabeled (with unknown abundances). As a result the ability to accurately predict endmember abundances of surface samples is as important as the capacity to generate spectra from hypothetical abundances, e.g. endmembers from abundances sampled from the corner of a simplex. We ...More

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