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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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  • CODES
    • Anomaly
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    • Classification of Hyperspectral Images
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  MiSiCNet: Minimum Simplex Convolutional Network for Deep Hyperspectral Unmixing

Hyperspectral Image Unmixing With Endmember Bundles and Group Sparsity Inducing Mixed Norms  

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