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Proceedings Paper

SHARE 2012: subpixel detection and unmixing experiments
Author(s): John P. Kerekes; Kyle Ludgate; AnneMarie Giannandrea; Nina G. Raqueno; Daniel S. Goldberg
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Paper Abstract

The quantitative evaluation of algorithms applied to remotely sensed hyperspectral imagery require data sets with known ground truth. A recent data collection known as SHARE 2012, conducted by scientists in the Digital Imaging and Remote Sensing Laboratory at the Rochester Institute of Technology together with several outside collaborators, acquired hyperspectral data with this goal in mind. Several experiments were designed, deployed, and ground truth collected to support algorithm evaluation. In this paper, we describe two experiments that addressed the particular needs for the evaluation of subpixel detection and unmixing algorithms. The subpixel detection experiment involved the deployment of dozens of nearly identical subpixel targets in a random spatial array. The subpixel targets were pieces of wood painted either green or yellow. They were sized to occupy about 5% to 20% of the 1 m pixels. The unmixing experiment used novel targets with prescribed fractions of different materials based on a geometric arrangement of subpixel patterns. These targets were made up of different fabrics with various colors. Whole pixel swatches of the same materials were also deployed in the scene to provide in-scene endmembers. Alternatively, researchers can use the unmixing targets alone to derive endmembers from the mixed pixels. Field reflectance spectra were collected for all targets and adjacent background areas. While efforts are just now underway to evaluate the detection performance using the subpixel targets, initial results for the unmixing targets have demonstrated retrieved fractions that are close approximations to the geometric fractions. These data, together with the ground truth, are planned to be made available to the remote sensing research community for evaluation and development of detection and unmixing algorithms.

Paper Details

Date Published: 18 May 2013
PDF: 7 pages
Proc. SPIE 8743, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XIX, 87430H (18 May 2013); doi: 10.1117/12.2016274
Show Author Affiliations
John P. Kerekes, Rochester Institute of Technology (United States)
Kyle Ludgate, Rochester Institute of Technology (United States)
AnneMarie Giannandrea, Rochester Institute of Technology (United States)
Nina G. Raqueno, Rochester Institute of Technology (United States)
Daniel S. Goldberg, 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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