A New Deep Convolutional Network for Effective Hyperspectral Unmixing-Reference-Cited by-同舟云学术

A New Deep Convolutional Network for Effective Hyperspectral Unmixing

Published:2022 Issue: Volume:15 Page:6999-7012
ISSN:1939-1404
Container-title:IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
language:
Short-container-title:IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

Author:

Tao Xuanwen¹^ORCID,Paoletti Mercedes E.²^ORCID,Han Lirong¹^ORCID,Wu Zhaoyue¹^ORCID,Ren Peng³^ORCID,Plaza Javier¹^ORCID,Plaza Antonio¹^ORCID,Haut Juan M.¹^ORCID

Affiliation:

1. Hyperspectral Computing Laboratory, Department of Technology of Computers and Communications, University of Extremadura, Cáceres, Spain

2. Department of Computer Architecture and Automatics, Complutense University of Madrid, Madrid, Spain

3. College of Information and Control Engineering, China University of Petroleum (East China), Qingdao, China

Funder

Consejería de Economía

Ciencia y Agenda Digital of the Junta de Extremadura

European Regional Development Fund of the European Union

Spanish Ministerio de Ciencia e Innovacion

2021 Leonardo Grant for Researchers and Cultural Creators

Fundación BBVA

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Atmospheric Science,Computers in Earth Sciences

Link

http://xplorestaging.ieee.org/ielx7/4609443/9656571/09864242.pdf?arnumber=9864242

Reference50 articles.

1. Fully constrained least squares linear spectral mixture analysis method for material quantification in hyperspectral imagery

2. Endmember Estimation From Hyperspectral Images Using Geometric Distances

3. Nonlinear Unmixing of Hyperspectral Images Using a Generalized Bilinear Model

4. Nonlinear Hyperspectral Unmixing With Robust Nonnegative Matrix Factorization

5. Fast algorithms for robust hyperspectral endmember extraction based on worst-case simplex volume maximization

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