Share Email Print
cover

Proceedings Paper

A novel latent gaussian copula framework for modeling spatial correlation in quantized SAR imagery with applications to ATR
Author(s): Brian T. Thelen; Ismael J. Xique; Joseph W. Burns; G. Steven Goley; Adam R. Nolan; Jonathan W. Benson
Format Member Price Non-Member Price
PDF $14.40 $18.00

Paper Abstract

With all of the new remote sensing modalities available, and with ever increasing capabilities and frequency of collection, there is a desire to fundamentally understand/quantify the information content in the collected image data relative to various exploitation goals, such as detection/classification. A fundamental approach for this is the framework of Bayesian decision theory, but a daunting challenge is to have significantly flexible and accurate multivariate models for the features and/or pixels that capture a wide assortment of distributions and dependen- cies. In addition, data can come in the form of both continuous and discrete representations, where the latter is often generated based on considerations of robustness to imaging conditions and occlusions/degradations. In this paper we propose a novel suite of ”latent” models fundamentally based on multivariate Gaussian copula models that can be used for quantized data from SAR imagery. For this Latent Gaussian Copula (LGC) model, we derive an approximate, maximum-likelihood estimation algorithm and demonstrate very reasonable estimation performance even for the larger images with many pixels. However applying these LGC models to large dimen- sions/images within a Bayesian decision/classification theory is infeasible due to the computational/numerical issues in evaluating the true full likelihood, and we propose an alternative class of novel pseudo-likelihoood detection statistics that are computationally feasible. We show in a few simple examples that these statistics have the potential to provide very good and robust detection/classification performance. All of this framework is demonstrated on a simulated SLICY data set, and the results show the importance of modeling the dependencies, and of utilizing the pseudo-likelihood methods.

Paper Details

Date Published: 28 April 2017
PDF: 10 pages
Proc. SPIE 10201, Algorithms for Synthetic Aperture Radar Imagery XXIV, 102010G (28 April 2017); doi: 10.1117/12.2267833
Show Author Affiliations
Brian T. Thelen, Michigan Tech Research Institute (United States)
Ismael J. Xique, Michigan Tech Research Institute (United States)
Joseph W. Burns, Michigan Tech Research Institute (United States)
G. Steven Goley, Etegent Technologies, Ltd. (United States)
Adam R. Nolan, Etegent Technologies, Ltd. (United States)
Jonathan W. Benson, Leidos, Inc. (United States)


Published in SPIE Proceedings Vol. 10201:
Algorithms for Synthetic Aperture Radar Imagery XXIV
Edmund Zelnio; Frederick D. Garber, Editor(s)

© SPIE. Terms of Use
Back to Top