An Elliptic Kernel Unsupervised Autoencoder—Graph Convolutional Network Ensemble Model for Hyperspectral Unmixing

An Elliptic Kernel Unsupervised Autoencoder—Graph Convolutional Network Ensemble Model for Hyperspectral Unmixing

Spectral unmixing is an important technique in remote sensing for analyzing hyperspectral images to identify endmembers and estimate fractional abundance maps. Over the past few decades, significant progress has been made in deep learning methods for endmember extraction and abundance estimation. Th...

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Bibliographic Details
Main Authors: Estefania Alfaro-Mejia, Carlos J. Delgado, Vidya Manian
Format: Article
Language:English
Published: IEEE 2025-01-01
Series:IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Subjects:
Abundance maps
convolutional autoencoder
endmembers
ensemble
graph convolutional network (GCN)
hyperspectral images (HSIs)
Online Access:https://ieeexplore.ieee.org/document/11021649/
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