Spatial-Spectral Schroedinger Eigenmaps

Version 1.1.0.0 (8.95 KB) by Nathan Cahill
Graph-based dimensionality reduction technique for image data
5.0
(2)
1.3K Downloads
Updated 19 Aug 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.

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 .

MATLAB Release Compatibility
Created with R2014a
Compatible with any release
Platform Compatibility
Windows macOS Linux

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SSSE/

Version Published Release Notes
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