SLIC Superpixels for Efficient Graph-Based Dimensionality Reduction of Hyperspectral Imagery
Improving graph-based dimensionality reduction techniques for image data to incorporate superpixels
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Performs SLIC superpixel-based dimensionality reduction of hyperspectral imagery, followed by SVM-based classification, as described in the paper:
X. Zhang, S. E. Chew, Z. Xu, and N. D. Cahill, "SLIC Superpixels for Efficient Graph-Based Dimensionality Reduction of Hyperspectral Imagery," Proc. SPIE Defense & Security: Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XXI, April 2015.
Cite As
Nathan Cahill (2026). SLIC Superpixels for Efficient Graph-Based Dimensionality Reduction of Hyperspectral Imagery (https://www.mathworks.com/matlabcentral/fileexchange/50184-slic-superpixels-for-e-cient-graph-based-dimensionality-reduction-of-hyperspectral-imagery), MATLAB Central File Exchange. Retrieved .
Acknowledgements
Inspired by: Spatial-Spectral Schroedinger Eigenmaps
General Information
- Version 1.1 (10.5 KB)
MATLAB Release Compatibility
- Compatible with any release
Platform Compatibility
- Windows
- macOS
- Linux
| Version | Published | Release Notes | Action |
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| 1.1 | Modifed zip file to remove extra folder level |
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| 1.0.0.0 |
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