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home/Knowledge Base/CODES/Classification of Hyperspectral Images/SVM-and MRF-Based Method for Accurate Classification of Hyperspectral Images

SVM-and MRF-Based Method for Accurate Classification of Hyperspectral Images

March 5, 2018

SVM- and MRF-Based Method for Accurate Classification of Hyperspectral Images
Yuliya Tarabalka, Mathieu Fauvel, Jocelyn Chanussot  and Jón Atli Benediktsson

IEEE Geoscience and Remote Sensing Letters, Vol. 7, no. 4, OCTOBER 2010
DOI: 10.1109/LGRS.2010.2047711

Year: 2010, Volume: 7, Issue: 4, Pages: 736 – 740

Archive 1.zip contains original codes from Dr Tarabalka’s PhD.
Archive 2.zip contains MRF codes using graph-cut which are much more efficient, also provided by Dr Tarabalka.

More code :
You can find a simplified (python) version of the algorithm here
(without gradiant and with ICM rather than Gibs sampler):

https://github.com/mfauvel/SummerSchoolGRSS/blob/master/Codes/script_mrf.py

Tags:classificationhyperspectral imagingMarkov random field (MRF)support vector machine (SVM)
Attached Files
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1 .pdf 341.77 KB 2010_IEEE_GRSL_SVM_MRF
2 .zip 10.88 KB Archive 1
3 .zip 9.68 KB Archive 2
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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
    • Graphs
    • 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

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

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