Share Email Print
cover

Proceedings Paper

Manifold alignment with Schroedinger eigenmaps
Format Member Price Non-Member Price
PDF $14.40 $18.00

Paper Abstract

The sun-target-sensor angle can change during aerial remote sensing. In an attempt to compensate BRDF effects in multi-angular hyperspectral images, the Semi-Supervised Manifold Alignment (SSMA) algorithm pulls data from similar classes together and pushes data from different classes apart. SSMA uses Laplacian Eigenmaps (LE) to preserve the original geometric structure of each local data set independently. In this paper, we replace LE with Spatial-Spectral Schoedinger Eigenmaps (SSSE) which was designed to be a semisupervised enhancement to the to extend the SSMA methodology and improve classification of multi-angular hyperspectral images captured over Hog Island in the Virginia Coast Reserve.

Paper Details

Date Published: 17 May 2016
PDF: 11 pages
Proc. SPIE 9840, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XXII, 98401K (17 May 2016); doi: 10.1117/12.2224068
Show Author Affiliations
Juan E. Johnson, Rochester Institute of Technology (United States)
Charles M. Bachmann, Rochester Institute of Technology (United States)
Nathan D. Cahill, Rochester Institute of Technology (United States)


Published in SPIE Proceedings Vol. 9840:
Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XXII
Miguel Velez-Reyes; David W. Messinger, Editor(s)

© SPIE. Terms of Use
Back to Top