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Proceedings Paper

Exploration of integrated visible to near-, shortwave-, and longwave-infrared (full range) hyperspectral data analysis
Author(s): Shelli R. Cone; Fred A. Kruse; Meryl L. McDowell
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Paper Abstract

Visible to near-, shortwave-, and longwave-infrared (VNIR, SWIR, LWIR) hyperspectral data were integrated using a variety of approaches to take advantage of complementary wavelength-specific spectral characteristics for improved material classification. The first approach applied separate minimum noise fraction (MNF) transforms to the three regions and combined only non-noise transformed bands. A second approach integrated the VNIR, SWIR, and LWIR data before using MNF analysis to isolate linear band combinations containing high signal to noise. Spectral endmembers extracted from each integrated dataset were unmixed and spatially mapped using a partial unmixing approach. Integrated results were compared to baseline analyses of the separate spectral regions. Outcomes show that analyzing across the full VNIR-SWIR-LWIR spectrum improves material characterization and identification.

Paper Details

Date Published: 21 May 2015
PDF: 12 pages
Proc. SPIE 9472, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XXI, 94721D (21 May 2015); doi: 10.1117/12.2086670
Show Author Affiliations
Shelli R. Cone, Scitor Corp. (United States)
Fred A. Kruse, Naval Postgraduate School (United States)
Meryl L. McDowell, Scitor Corp. (United States)
Naval Postgraduate School (United States)


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

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