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

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

Remotely sensed data are dominated by mixed land use and land cover (LULC) types. Spectral unmixing (SU) is a key technique that disentangles mixed pixels into constituent LULC types and their abundance fractions. While existing studies on deep learning (DL) for SU typically focus on single time-ste...

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Bibliographic Details
Main Authors: Jose Rodriguez-Ortega, Rohaifa Khaldi, Domingo Alcaraz-Segura, Siham Tabik
Format: Article
Language:English
Published: IEEE 2024-01-01
Series:IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Subjects:
Abundance estimation
bidirectional long-short term memory (LSTM)
climatic data
deep learning (DL)
geo-topographic data
land use and land cover (LULC)
Online Access:https://ieeexplore.ieee.org/document/10416323/

Internet

https://ieeexplore.ieee.org/document/10416323/

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