Spatial-Spectral Schroedinger Eigenmaps

Version 1.1.0.0 (8,95 ko) par Nathan Cahill
Graph-based dimensionality reduction technique for image data
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Mise à jour 19 août 2014

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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.

Citation pour cette source

Nathan Cahill (2026). Spatial-Spectral Schroedinger Eigenmaps (https://fr.mathworks.com/matlabcentral/fileexchange/45908-spatial-spectral-schroedinger-eigenmaps), MATLAB Central File Exchange. Extrait(e) le .

Compatibilité avec les versions de MATLAB
Créé avec R2014a
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1.1.0.0

Updated the original version of SSSE as described in the SPIE conference paper to the newer version as described in the submitted journal article. Also included code for subsequent classification with SVMs.
1.0.0.0