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Proceedings Paper

Piecewise flat embeddings for hyperspectral image analysis
Author(s): Tyler L. Hayes; Renee T. Meinhold; John F. Hamilton; Nathan D. Cahill
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Paper Abstract

Graph-based dimensionality reduction techniques such as Laplacian Eigenmaps (LE), Local Linear Embedding (LLE), Isometric Feature Mapping (ISOMAP), and Kernel Principal Components Analysis (KPCA) have been used in a variety of hyperspectral image analysis applications for generating smooth data embeddings. Recently, Piecewise Flat Embeddings (PFE) were introduced in the computer vision community as a technique for generating piecewise constant embeddings that make data clustering / image segmentation a straightforward process. In this paper, we show how PFE arises by modifying LE, yielding a constrained ℓ1-minimization problem that can be solved iteratively. Using publicly available data, we carry out experiments to illustrate the implications of applying PFE to pixel-based hyperspectral image clustering and classification.

Paper Details

Date Published: 5 May 2017
PDF: 11 pages
Proc. SPIE 10198, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XXIII, 101980O (5 May 2017); doi: 10.1117/12.2262302
Show Author Affiliations
Tyler L. Hayes, Rochester Institute of Technology (United States)
Renee T. Meinhold, Rochester Institute of Technology (United States)
John F. Hamilton, Rochester Institute of Technology (United States)
Nathan D. Cahill, Rochester Institute of Technology (United States)


Published in SPIE Proceedings Vol. 10198:
Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XXIII
Miguel Velez-Reyes; David W. Messinger, Editor(s)

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