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home/Knowledge Base/CODES/Spectral Unmixing/SUnGP: A greedy sparse approximation algorithm for hyperspectral unmixing

SUnGP: A greedy sparse approximation algorithm for hyperspectral unmixing

December 12, 2016

SUnGP: A greedy sparse approximation algorithm for hyperspectral unmixing
N. Akhtar, F. Shafait and A. Mian

ICPR 2014
Publication Year: 2014

Tags:hyperspectral unmixing
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1 .pdf 1.23 MB Naveed_ICPR2014
2 .zip 626.87 KB SUnGP_demo
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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
  • DATA

  CyCU-Net: Cycle-Consistency Unmixing Network by Learning Cascaded Autoencoders

Futuristic greedy approach to sparse unmixing of hyperspectral data  

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