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home/Knowledge Base/CODES/Spectral Unmixing/Bayesian Unmixing of Hyperspectral Image Sequence With Composite Priors for Abundance and Endmember Variability

Bayesian Unmixing of Hyperspectral Image Sequence With Composite Priors for Abundance and Endmember Variability

April 6, 2021

Bayesian Unmixing of Hyperspectral Image Sequence With Composite Priors for Abundance and Endmember Variability
H. Liu, Y. Lu, Z. Wu, Q. Du, J. Chanussot and Z. Wei

IEEE Transactions on Geoscience and Remote Sensing
DOI: 10.1109/TGRS.2021.3064708

Tags:bayesian unmixingendmember variabilityhyperspectral imageMarkov chain Monte Carlo method
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Categories
  • CODES
    • Anomaly
    • Change Detection
    • Classification of Hyperspectral Images
    • Classification of Remote Sensing Data
    • Data fusion: hyperspectral + Lidar
    • Data fusion: Hyperspectral + Multispectral
    • Deep Learning
    • Denoising
    • Feature Extraction
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    • Graphs, Manifold
    • Hyperspectral
    • Hyperspectral remote sensing
    • Hyperspectral Super Resolution
    • Infrared
    • Machine Learning in Remote Sensing
    • Multi-modal
    • Object Detection
    • Pansharpening
    • Registration
    • Sequences
    • Spectral Unmixing
    • Super Resolution
    • Synthetic Aperture Radar and Radar Sounder
    • Target Detection
    • Tensor
    • Transformer
  • DATA

  CoSpace: Common Subspace Learning from Hyperspectral-Multispectral Correspondences

A Trainable Spectral-Spatial Sparse Coding Model for Hyperspectral Image Restoration  

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