Incorporating signal-dependent noise for hyperspectral target detection

Christopher J. Morman; Joseph Meola

doi:10.1117/12.2176391

21 May 2015 Incorporating signal-dependent noise for hyperspectral target detection

Christopher J. Morman, Joseph Meola

Proceedings Volume 9472, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XXI; 94720M (2015) https://doi.org/10.1117/12.2176391
Event: SPIE Defense + Security, 2015, Baltimore, MD, United States

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.

Citation Download Citation

Christopher J. Morman and Joseph Meola "Incorporating signal-dependent noise for hyperspectral target detection", Proc. SPIE 9472, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XXI, 94720M (21 May 2015); https://doi.org/10.1117/12.2176391

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