18 August 2009 Comparison of basis-vector selection methods for structural modeling of hyperspectral imagery
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Proceedings Volume 7457, Imaging Spectrometry XIV; 74570C (2009); doi: 10.1117/12.835956
Event: SPIE Optical Engineering + Applications, 2009, San Diego, California, United States
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.
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Carolina Peña-Ortega, Miguel Vélez-Reyes, "Comparison of basis-vector selection methods for structural modeling of hyperspectral imagery", Proc. SPIE 7457, Imaging Spectrometry XIV, 74570C (18 August 2009); doi: 10.1117/12.835956; https://doi.org/10.1117/12.835956
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KEYWORDS
Target detection

Sensors

Data modeling

Hyperspectral imaging

Statistical modeling

3D modeling

Hyperspectral target detection

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