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Proceedings Paper

Biased normalized cuts for target detection in hyperspectral imagery
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Paper Abstract

The Biased Normalized Cuts (BNC) algorithm is a useful technique for detecting targets or objects in RGB imagery. In this paper, we propose modifying BNC for the purpose of target detection in hyperspectral imagery. As opposed to other target detection algorithms that typically encode target information prior to dimensionality reduction, our proposed algorithm encodes target information after dimensionality reduction, enabling a user to detect different targets in interactive mode. To assess the proposed BNC algorithm, we utilize hyperspectral imagery (HSI) from the SHARE 2012 data campaign, and we explore the relationship between the number and the position of expert-provided target labels and the precision/recall of the remaining targets in the scene.

Paper Details

Date Published: 17 May 2016
PDF: 11 pages
Proc. SPIE 9840, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XXII, 98400Y (17 May 2016); doi: 10.1117/12.2224067
Show Author Affiliations
Xuewen Zhang, Rochester Institute of Technology (United States)
Leidy P. Dorado-Munoz, Rochester Institute of Technology (United States)
David W. Messinger, Rochester Institute of Technology (United States)
Nathan D. Cahill, Rochester Institute of Technology (United States)


Published in SPIE Proceedings Vol. 9840:
Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XXII
Miguel Velez-Reyes; David W. Messinger, Editor(s)

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