Assessment of residual fixed pattern noise on hyperspectral detection performance

Carl J. Cusumano; Bradley M. Ratliff; Jason R. Kaufman; Joseph Meola

doi:10.1117/12.2519156

14 May 2019 Assessment of residual fixed pattern noise on hyperspectral detection performance

Carl J. Cusumano, Bradley M. Ratliff, Jason R. Kaufman, Joseph Meola

Proceedings Volume 10986, Algorithms, Technologies, and Applications for Multispectral and Hyperspectral Imagery XXV; 109861H (2019) https://doi.org/10.1117/12.2519156
Event: SPIE Defense + Commercial Sensing, 2019, Baltimore, MD, United States

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

Citation Download Citation

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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