Spatial-Spectral Schroedinger Eigenmaps
Performs dimensionality reduction and classification of hyperspectral imagery using the Spatial-Spectral Schroedinger Eigenmaps (SSSE) algorithm, as described in the papers:
1) N. D. Cahill, W. Czaja, and D. W. Messinger, "Schroedinger Eigenmaps with Nondiagonal Potentials for Spatial-Spectral Clustering of Hyperspectral Imagery," Proc. SPIE Defense & Security: Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XX, May 2014.
2) N. D. Cahill, W. Czaja, and D. W. Messinger, "Spatial-Spectral Schroedinger Eigenmaps for Dimensionality Reduction and Classification of Hyperspectral Imagery," submitted.
This example script also performs classification using Support Vector Machines, as described in paper 2.
Cite As
Nathan Cahill (2024). Spatial-Spectral Schroedinger Eigenmaps (https://www.mathworks.com/matlabcentral/fileexchange/45908-spatial-spectral-schroedinger-eigenmaps), MATLAB Central File Exchange. Retrieved .
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- Image Processing and Computer Vision > Computer Vision Toolbox > Point Cloud Processing > Display Point Clouds >
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Acknowledgements
Inspired: SLIC Superpixels for Efficient Graph-Based Dimensionality Reduction of Hyperspectral Imagery, Spatial-Spectral Dimensionality Reduction with Partial Knowledge of Class Labels
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