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home/Knowledge Base/CODES/Spectral Unmixing/Hyperspectral Unmixing Overview: Geometrical, Statistical, and Sparse Regression-Based Approaches

Hyperspectral Unmixing Overview: Geometrical, Statistical, and Sparse Regression-Based Approaches

February 6, 2015

Hyperspectral Unmixing Overview: Geometrical, Statistical, and Sparse Regression-Based Approaches
J. Bioucas-Dias, A. Plaza, N. Dobigeon, M. Parente, Q. Du, P. Gader and J. Chanussot

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Vol. 5 n. 2 – pp 354-379
DOI: 10.1109/JSTARS.2012.2194696 , 2012

Tags:hyperspectral imaginghyperspectral remote sensingimage analysisimage processingimaging spectroscopyinverse problemslinear mixturemachine learning algorithmsnonlinear mixturespattern recognitionremote sensingsparsityspectroscopyunmixing
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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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    • 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
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  • DATA

  MiSiCNet: Minimum Simplex Convolutional Network for Deep Hyperspectral Unmixing

Hyperspectral Pansharpening: Critical review, tools, and future perspectives  

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