Nonlinear Unmixing via Deep Autoencoder Networks for Generalized Bilinear Model

Nonlinear Unmixing via Deep Autoencoder Networks for Generalized Bilinear Model

Hyperspectral unmixing decomposes the observed mixed spectra into a collection of constituent pure material signatures and the associated fractional abundances. Because of the universal modeling ability of neural networks, deep learning (DL) techniques are gaining prominence in solving hyperspectral...

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Bibliographic Details
Main Authors: Jinhua Zhang, Xiaohua Zhang, Hongyun Meng, Caihao Sun, Li Wang, Xianghai Cao
Format: Article
Language:English
Published: MDPI AG 2022-10-01
Series:Remote Sensing
Subjects:
unsupervised nonlinear spectral unmixing
generalized bilinear model
deep learning
autoencoder network
Online Access:https://www.mdpi.com/2072-4292/14/20/5167

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https://www.mdpi.com/2072-4292/14/20/5167

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