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Unsupervised Feature-Learning for Hyperspectral Data with Autoencoders

November 9, 2019

Unsupervised Feature-Learning for Hyperspectral Data with Auto-encoders
Lloyd Windrim, Rishi Ramakrishnan, Arman Melkumyan, Richard J. Murphy and Anna Chlingaryan

Remote Sensing
doi: 10.3390/rs11070864
Year: 2019 , Volume: 11 , Issue: 7
Page: 864

documentation page

the source code on github repo

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1	.pdf	4.44 MB	remotesensing-11-00864

Sparsity-Enhanced Convolutional Decomposition: A Novel Tensor-Based Paradigm for Blind Hyperspectral Unmixing
Bayesian Unmixing of Hyperspectral Image Sequence With Composite Priors for Abundance and Endmember Variability
CyCU-Net: Cycle-Consistency Unmixing Network by Learning Cascaded Autoencoders
l₀-l₁ Hybrid Total Variation Regularization and Its Applications on Hyperspectral Image Mixed Noise Removal and Compressed Sensing
Tensor Low-Rank Constraint and l0 Total Variation for Hyperspectral Image Mixed Noise Removal
Learning Locality-Constrained Sparse Coding for Spectral Enhancement of Multispectral Imagery

Unsupervised Feature-Learning for Hyperspectral Data with Autoencoders

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