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Proceedings Paper

SHARE 2012: large edge targets for hyperspectral imaging applications
Author(s): Kelly Canham; Daniel Goldberg; John Kerekes; Nina Raqueno; David Messinger
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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
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)
Nina Raqueno, 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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