Manifold alignment with Schroedinger eigenmaps

Juan E. Johnson; Charles M. Bachmann; Nathan D. Cahill

doi:10.1117/12.2224068

17 May 2016 Manifold alignment with Schroedinger eigenmaps

Juan E. Johnson, Charles M. Bachmann, Nathan D. Cahill

Author Affiliations +

Proceedings Volume 9840, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XXII; 98401K (2016) https://doi.org/10.1117/12.2224068
Event: SPIE Defense + Security, 2016, Baltimore, MD, United States

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.

Citation Download Citation

Juan E. Johnson, Charles M. Bachmann, and Nathan D. Cahill "Manifold alignment with Schroedinger eigenmaps", Proc. SPIE 9840, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XXII, 98401K (17 May 2016); https://doi.org/10.1117/12.2224068

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