
Proceedings Paper
SHARE 2012: large edge targets for hyperspectral imaging applicationsFormat | Member Price | Non-Member Price |
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Paper Abstract
Spectral unmixing is a type of hyperspectral imagery (HSI) sub-pixel analysis where the constituent spectra and abundances
within the pixel are identified. However, validating the results obtained from spectral unmixing is very difficult due to a lack
of real-world data and ground-truth information associated with these real-world images. Real HSI data is preferred for
validating spectral unmixing, but when there is no HSI truth-data available, then validation of spectral unmixing algorithms
relies on user-defined synthetic images which can be generated to exploit the benefits (or hide the flaws) in the new
unmixing approaches. Here we introduce a new dataset (SHARE 2012: large edge targets) for the validation of spectral
unmixing algorithms. The SHARE 2012 large edge targets are uniform 9m by 9m square regions of a single material (grass,
sand, black felt, or white TyVek). The spectral profile and the GPS of the corners of the materials were recorded so that the
heading of the edge separating any two materials can be determined from the imagery. An estimate for the abundance of two
neighboring materials along a common edge can be calculated geometrically by identifying the edge which spans multiple
pixels. These geometrically calculated abundances can then be used as validation of spectral unmixing algorithms. The
size, shape, and spectral profiles of these targets also make them useful for radiometric calibration, atmospheric adjacency
effects, and sensor MTF calculations. The imagery and ground-truth information are presented here.
Paper Details
Date Published: 18 May 2013
PDF: 9 pages
Proc. SPIE 8743, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XIX, 87430G (18 May 2013); doi: 10.1117/12.2016271
Published in SPIE Proceedings Vol. 8743:
Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XIX
Sylvia S. Shen; Paul E. Lewis, Editor(s)
PDF: 9 pages
Proc. SPIE 8743, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XIX, 87430G (18 May 2013); doi: 10.1117/12.2016271
Show Author Affiliations
Kelly Canham, Rochester Institute of Technology (United States)
Daniel Goldberg, Rochester Institute of Technology (United States)
John Kerekes, Rochester Institute of Technology (United States)
Daniel Goldberg, Rochester Institute of Technology (United States)
John Kerekes, Rochester Institute of Technology (United States)
Nina Raqueno, Rochester Institute of Technology (United States)
David Messinger, Rochester Institute of Technology (United States)
David Messinger, Rochester Institute of Technology (United States)
Published in SPIE Proceedings Vol. 8743:
Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XIX
Sylvia S. Shen; Paul E. Lewis, Editor(s)
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