SLIC superpixels for efficient graph-based dimensionality reduction of hyperspectral imagery

Xuewen Zhang; Selene E. Chew; Zhenlin Xu; Nathan D. Cahill

doi:10.1117/12.2176911

21 May 2015 SLIC superpixels for efficient graph-based dimensionality reduction of hyperspectral imagery

Xuewen Zhang, Selene E. Chew, Zhenlin Xu, Nathan D. Cahill

Proceedings Volume 9472, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XXI; 947209 (2015) https://doi.org/10.1117/12.2176911
Event: SPIE Defense + Security, 2015, Baltimore, MD, United States

Abstract

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.

Citation Download Citation

Xuewen Zhang, Selene E. Chew, Zhenlin Xu, and Nathan D. Cahill "SLIC superpixels for efficient graph-based dimensionality reduction of hyperspectral imagery", Proc. SPIE 9472, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XXI, 947209 (21 May 2015); https://doi.org/10.1117/12.2176911

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