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Proceedings Paper

Estimation of single-point sea-surface brightness statistics (Conference Presentation)
Author(s): Kevin Nielson
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Paper Abstract

Detection system performance analysis is frequently performed assuming Gaussian background statistics, often for convenience or due to a lack of better information. The Gaussian background assumption creates a relationship between probability of detection and probability of false-alarm (the receiver operating characteristic curve or ROC curve) as a function of signal-to-noise ratio. When the background distribution is non-Gaussian (e.g., with strong skew or excess kurtosis), analysis of detection system performance based on the estimated variance of the background signal under the assumption of Gaussianity will result in misleading estimates of detection and false alarm probabilities. In order to correctly define the ROC curve, the background statistics must be known. For infrared imaging systems, one example of a background which may be strongly non-Gaussian is the radiance field of a wavy sea-surface. Although the sea-surface slope field is assumed to be a Gaussian random field, the radiance field maps nonlinearly to the slope field, producing the phenomenon of sun glitter. The result is strongly non-Gaussian radiance distribution functions for certain sea-surface viewing conditions. Based on an analytical expression for sea surface radiance due to Ross, Potvin, and Dion (2005), we construct an approximate analytic expression for the distribution function of single-point (i.e., correlated neither in time nor space) sea-surface radiance as observed by a passive, square-law, electro-optical/infrared detector. With this distribution function, the relationship between detection and false-alarm probabilities can be more accurately characterized.

Paper Details

Date Published: 5 October 2018
Proc. SPIE 10646, Signal Processing, Sensor/Information Fusion, and Target Recognition XXVII, 106461D (5 October 2018); doi: 10.1117/12.2304912
Show Author Affiliations
Kevin Nielson, Georgia Tech Research Institute (United States)

Published in SPIE Proceedings Vol. 10646:
Signal Processing, Sensor/Information Fusion, and Target Recognition XXVII
Ivan Kadar, Editor(s)

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