HapkeCNN: Blind Nonlinear Unmixing for Intimate Mixtures Using Hapke Model and Convolutional Neural Network
Author:
Affiliation:
1. Machine Learning Group, Helmholtz-Zentrum Dresden-Rossendorf, Helmholtz Institute Freiberg for Resource Technology, Freiberg, Germany
2. Department of Physics, Imec-Visionlab, University of Antwerp (CDE), Antwerp, Belgium
Funder
Alexander-von-Humboldt-Stiftung/Foundation
Research Foundation-Flanders
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
General Earth and Planetary Sciences,Electrical and Electronic Engineering
Link
http://xplorestaging.ieee.org/ielx7/36/9633014/09869671.pdf?arnumber=9869671
Reference45 articles.
1. Collaborative nonnegative matrix factorization for remotely sensed hyperspectral unmixing
2. ICE: a statistical approach to identifying endmembers in hyperspectral images
3. Theory of Reflectance and Emittance Spectroscopy
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