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Assessment of residual fixed pattern noise on hyperspectral detection performance
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Paper Abstract

Hyperspectral imaging sensors suffer from pixel-to-pixel response nonuniformity that manifests as fixed pattern noise (FPN) in collected data. FPN is typically removed by application of flat-field calibration procedures and nonuniformity correction algorithms. Despite application of these techniques, some amount of residual fixed pattern noise (RFPN) may persist in the data, negatively impacting target detection performance. In this paper we examine the conditions under which RFPN can impact detection performance using data collected in the SWIR across a range of target materials. We examine the application of scene-based nonuniformity correction (SBNUC) algorithms and assess their ability to remove RFPN. Moreover, we examine the effect of RFPN after application of these techniques to assess detection performance on a number of target materials that range in inherent separability from the background.

Paper Details

Date Published: 14 May 2019
PDF: 13 pages
Proc. SPIE 10986, Algorithms, Technologies, and Applications for Multispectral and Hyperspectral Imagery XXV, 109861H (14 May 2019); doi: 10.1117/12.2519156
Show Author Affiliations
Carl J. Cusumano, Univ. of Dayton Research Institute (United States)
Bradley M. Ratliff, Univ. of Dayton Research Institute (United States)
Jason R. Kaufman, Univ. of Dayton Research Institute (United States)
Joseph Meola, Air Force Research Lab. (United States)


Published in SPIE Proceedings Vol. 10986:
Algorithms, Technologies, and Applications for Multispectral and Hyperspectral Imagery XXV
Miguel Velez-Reyes; David W. Messinger, Editor(s)

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