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home/Knowledge Base/CODES/Classification of Hyperspectral Images/Spectral–Spatial Classification for Hyperspectral Data Using Rotation Forests With Local Feature Extraction and Markov Random Fields

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

February 16, 2015

Spectral–Spatial Classification for Hyperspectral Data Using Rotation Forests With Local Feature Extraction and Markov Random Fields
Junshi Xia ; Chanussot, J. ; Peijun Du ; Xiyan He

Geoscience and Remote Sensing, IEEE Transactions on
Volume: 53 , Issue: 5
DOI: 10.1109/TGRS.2014.2361618
Publication Year: 2015 , Page(s): 2532 – 2546

The implementation of the random forests is based on the freely available software “Weka”:
http://www.cs.waikato.ac.nz/~ml/weka/

Tags:feature extractionhyperspectral image classificationMarkov random fields (MRFs)rotation forests
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  SVM-and MRF-Based Method for Accurate Classification of Hyperspectral Images

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

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