DAEN: Deep Autoencoder Networks for Hyperspectral Unmixing

Spectral unmixing is a technique for remotely sensed image interpretation that expresses each (possibly mixed) pixel as a combination of pure spectral signatures (endmembers) and their fractional abundances. In this paper, we develop a new technique for unsupervised unmixing which is based on a deep...

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Vydané v:	IEEE transactions on geoscience and remote sensing Ročník 57; číslo 7; s. 4309 - 4321
Hlavní autori:	Su, Yuanchao, Li, Jun, Plaza, Antonio, Marinoni, Andrea, Gamba, Paolo, Chakravortty, Somdatta
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York IEEE 01.07.2019 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Computers Deep autoencoder network (DAEN) deep learning endmember identification Estimation Hyperspectral imaging hyperspectral unmixing Noise reduction Outliers (statistics) Remote sensing Signal processing Signal to noise ratio Spectra Spectral signatures Training variational autoencoder (VAE)
ISSN:	0196-2892, 1558-0644
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