Biased normalized cuts for target detection in hyperspectral imagery

Xuewen Zhang; Leidy P. Dorado-Munoz; David W. Messinger; Nathan D. Cahill

doi:10.1117/12.2224067

17 May 2016 Biased normalized cuts for target detection in hyperspectral imagery

Xuewen Zhang, Leidy P. Dorado-Munoz, David W. Messinger, Nathan D. Cahill

Proceedings Volume 9840, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XXII; 98400Y (2016) https://doi.org/10.1117/12.2224067
Event: SPIE Defense + Security, 2016, Baltimore, MD, United States

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.

Citation Download Citation

Xuewen Zhang, Leidy P. Dorado-Munoz, David W. Messinger, and Nathan D. Cahill "Biased normalized cuts for target detection in hyperspectral imagery", Proc. SPIE 9840, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XXII, 98400Y (17 May 2016); https://doi.org/10.1117/12.2224067

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