MultiHU-TD: Multifeature Hyperspectral Unmixing Based on Tensor Decomposition-Reference-Cited by-同舟云学术

MultiHU-TD: Multifeature Hyperspectral Unmixing Based on Tensor Decomposition

Published:2023 Issue: Volume:61 Page:1-21
ISSN:0196-2892
Container-title:IEEE Transactions on Geoscience and Remote Sensing
language:
Short-container-title:IEEE Trans. Geosci. Remote Sensing

Author:

Jouni Mohamad¹^ORCID,Mura Mauro Dalla²^ORCID,Drumetz Lucas³^ORCID,Comon Pierre¹^ORCID

Affiliation:

1. Centre National de la Recherche Scientifique (CNRS), Institut National de Recherche en Informatique et en Automatique (Inria), Grenoble Institute of Technology (Grenoble INP), Grenoble Images Speech Signals and Automatics Laboratory (GIPSA-Lab), Université Grenoble Alpes, Grenoble, France

2. CNRS, Inria, Grenoble Institute of Technology (Grenoble INP), Grenoble Images Speech Signals and Automatics Laboratory (GIPSA-Lab), Université Grenoble Alpes, Grenoble, France

3. Institut Mines-Télécom (IMT) Atlantique, Laboratoire des Sciences et Techniques de l’Information de la Communication et de la Connaissance (Lab-STICC), Unité Mixte de Recherche (UMR) CNRS6285, Brest, France

Funder

Agence Nationale de la Recherche (ANR) FUsion MULTIspectral-imSPOC

Région Auvergne-Rhône-Alpes grant “Pack Ambition International 2021”

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Earth and Planetary Sciences,Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/36/10006360/10251420.pdf?arnumber=10251420

Reference60 articles.

1. Learning Endmember Dynamics in Multitemporal Hyperspectral Data Using A State-Space Model Formulation

2. The Evolution of the Morphological Profile: from Panchromatic to Hyperspectral Images