Nonlinear graph-based dimensionality reduction algorithms such as Laplacian Eigenmaps (LE) and Schroedinger Eigenmaps (SE) have been shown to be very effective at yielding low-dimensional representations of hyperspectral image data. However, the steps of graph construction and eigenvector computation required by LE and SE can be prohibitively costly as the number of image pixels grows. In this paper, we propose pre-clustering the hyperspectral image into Simple Linear Iterative Clustering (SLIC) superpixels and then performing LE- or SE-based dimensionality reduction with the superpixels as input. We then investigate how different superpixel size and regularity choices yield trade-offs between improvements in computational efficiency and accuracy of subsequent classification using the low-dimensional representations.

Date Published: 21 May 2015
PDF: 14 pages
Proc. SPIE 9472, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XXI, 947209 (21 May 2015); doi: 10.1117/12.2176911

Published in SPIE Proceedings Vol. 9472:
Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XXI
Miguel Velez-Reyes; Fred A. Kruse, Editor(s)