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14 May 2019 Assessment of residual fixed pattern noise on hyperspectral detection performance
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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.
Conference Presentation
© (2019) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Carl J. Cusumano, Bradley M. Ratliff, Jason R. Kaufman, and Joseph Meola "Assessment of residual fixed pattern noise on hyperspectral detection performance", Proc. SPIE 10986, Algorithms, Technologies, and Applications for Multispectral and Hyperspectral Imagery XXV, 109861H (14 May 2019); https://doi.org/10.1117/12.2519156
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