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home/Knowledge Base/CODES/Hyperspectral/SGML: A Symmetric Graph Metric Learning Framework for Efficient Hyperspectral Image Classification

SGML: A Symmetric Graph Metric Learning Framework for Efficient Hyperspectral Image Classification

December 27, 2021

SGML: A Symmetric Graph Metric Learning Framework for Efficient Hyperspectral Image Classification
Y. Li, B. Xi, J. Li, R. Song, Y. Xiao and J. Chanussot

in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing

DOI : 10.1109/JSTARS.2021.3135548

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1 .pdf 8.06 MB SGML_A_Symmetric_Graph_Metric_Learning_Framework_for_Efficient_Hyperspectral_Image_Classification
2 .zip 11.99 MB JSTARS_2021_SGML-main
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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
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    • Hyperspectral Super Resolution
    • Infrared
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    • Target Detection
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  • DATA

  Spectral–Spatial Classification for Hyperspectral Data Using Rotation Forests With Local Feature Extraction and Markov Random Fields

Semisupervised Cross-Scale Graph Prototypical Network for Hyperspectral Image Classification  

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