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Proceedings Paper

Comparison of basis-vector selection methods for structural modeling of hyperspectral imagery
Author(s): Carolina Peña-Ortega; Miguel Vélez-Reyes
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Paper Abstract

This paper presents a comparison of different methods for structural modeling of hyperspectral imagery for target detection. We study structured models, based on linear subspaces and convex polyhedral cones, and their application for target detection. Different training methods are studied: Singular Value Decomposition (SVD) is used for subspace modeling, and Maximum Distance (MaxD) and Positive Matrix Factorization (PMF) for convex polyhedral modeling. We study different detectors based on orthogonal and oblique projections for subspace and convex polyhedral cones and evaluate their performance. Experimental results using HYDICE imagery are presented.

Paper Details

Date Published: 18 August 2009
PDF: 10 pages
Proc. SPIE 7457, Imaging Spectrometry XIV, 74570C (18 August 2009); doi: 10.1117/12.835956
Show Author Affiliations
Carolina Peña-Ortega, Univ. of Puerto Rico (United States)
Miguel Vélez-Reyes, Univ. of Puerto Rico (United States)

Published in SPIE Proceedings Vol. 7457:
Imaging Spectrometry XIV
Sylvia S. Shen; Paul E. Lewis, Editor(s)

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