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Proceedings Paper

Incorporating signal-dependent noise for hyperspectral target detection
Author(s): Christopher J. Morman; Joseph Meola
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Paper Abstract

The majority of hyperspectral target detection algorithms are developed from statistical data models employing stationary background statistics or white Gaussian noise models. Stationary background models are inaccurate as a result of two separate physical processes. First, varying background classes often exist in the imagery that possess different clutter statistics. Many algorithms can account for this variability through the use of subspaces or clustering techniques. The second physical process, which is often ignored, is a signal-dependent sensor noise term. For photon counting sensors that are often used in hyperspectral imaging systems, sensor noise increases as the measured signal level increases as a result of Poisson random processes. This work investigates the impact of this sensor noise on target detection performance. A linear noise model is developed describing sensor noise variance as a linear function of signal level. The linear noise model is then incorporated for detection of targets using data collected at Wright Patterson Air Force Base.

Paper Details

Date Published: 21 May 2015
PDF: 13 pages
Proc. SPIE 9472, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XXI, 94720M (21 May 2015); doi: 10.1117/12.2176391
Show Author Affiliations
Christopher J. Morman, Univ. of Dayton (United States)
Joseph Meola, Air Force Research Lab. (United States)


Published in SPIE Proceedings Vol. 9472:
Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XXI
Miguel Velez-Reyes; Fred A. Kruse, Editor(s)

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