Share Email Print

Proceedings Paper

Analytic performance model for grayscale quantization in the presence of additive noise
Author(s): Adam R. Nolan; G. Steven Goley
Format Member Price Non-Member Price
PDF $17.00 $21.00

Paper Abstract

Synthetic aperture radar (SAR) exploitation algorithms typically rely on the use of derived features to represent the target. These features are chosen to discriminate between target classes while exhibiting robustness to noise and calibration artifacts. One of the challenges in working with such features, is understanding when this assumption of robustness is no longer valid. In this paper, we focus on characterizing the performance of the gray scale quantization feature in the presence of additive noise. We derive an approximation for the variance of the intraclass distance by treating the additive noise as an independently identically distributed (iid) process. The analytic model is contrasted with empirical results for a two class problem.

Paper Details

Date Published: 19 May 2011
PDF: 10 pages
Proc. SPIE 8049, Automatic Target Recognition XXI, 804910 (19 May 2011); doi: 10.1117/12.884131
Show Author Affiliations
Adam R. Nolan, Etegent Technologies Ltd. (United States)
G. Steven Goley, Etegent Technologies Ltd. (United States)

Published in SPIE Proceedings Vol. 8049:
Automatic Target Recognition XXI
Firooz A. Sadjadi; Abhijit Mahalanobis, Editor(s)

© SPIE. Terms of Use
Back to Top
Sign in to read the full article
Create a free SPIE account to get access to
premium articles and original research
Forgot your username?