Bidirectional Recurrent Imputation and Abundance Estimation of LULC Classes With MODIS Multispectral Time-Series and Geo-Topographic and Climatic Data-Reference-Cited by-同舟云学术

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Bidirectional Recurrent Imputation and Abundance Estimation of LULC Classes With MODIS Multispectral Time-Series and Geo-Topographic and Climatic Data

Published:2024 Issue: Volume:17 Page:4626-4645
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:

Rodríguez-Ortega José¹^ORCID,Khaldi Rohaifa¹^ORCID,Alcaraz-Segura Domingo²^ORCID,Tabik Siham¹^ORCID

Affiliation:

1. Department of Computer Science and Artificial Intelligence, Andalusian Research Institute in Data Science and Computational Intelligence (DaSCI), University of Granada, Granada, Spain

2. Department of Botany, Faculty of Science and with the iEcolab, Andalusian Institute for Earth System Research (IISTA-CEAMA), University of Granada, Granada, Spain

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/4609443/10330207/10416323.pdf?arnumber=10416323

Reference88 articles.

1. Hyperspectral Unmixing via Deep Convolutional Neural Networks

2. Spectral unmixing

3. Deep Learning in Hyperspectral Unmixing: A Review

4. Spatio-temporal spectral unmixing of time-series images

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