Conditionally gamma- and k-distribution models for SAR ATR

Michael D. DeVore; Joseph A. O'Sullivan; Lee J. Montagnino

doi:10.1117/12.478690

1 August 2002 Conditionally gamma- and k-distribution models for SAR ATR

Michael D. DeVore, Joseph A. O'Sullivan, Lee J. Montagnino

Proceedings Volume 4727, Algorithms for Synthetic Aperture Radar Imagery IX; (2002) https://doi.org/10.1117/12.478690
Event: AeroSense 2002, 2002, Orlando, FL, United States

Abstract

Model-based approaches to automatic target recognition (ATR) generally infer the class and pose of objects in imagery by exploiting theoretical models of the formed images. Recently, we have performed an evaluation of several statistical models for synthetic aperture radar (SAR) and have conducted experiments with ATR algorithms derived from these models. In particular, a one-parameter complex Gaussian model, classically used to model diffuse scattering, was shown to deliver higher recognition rates than a one-parameter quarter-power normal model on actual SAR data. However an extended, two-parameter quarter-power model was consistently a better fit to the data than a corresponding two-parameter Gaussian model. In this paper, we apply Rician, gamma, and K distribution models, which are two-parameter extensions of the complex Gaussian and quarter-power normal models, to ATR from SAR magnitude imagery. We consider maximum-likelihood estimation of unknown model parameters and apply the resulting training and testing algorithms to actual SAR data. We show that the K distribution model performs better than the Rician and gamma models for both large and small sample sizes. The one-parameter complex Gaussian model performed slightly better than the K model overall. For small sample sizes, this is likely due to the relative stability in estimating only one model parameter. For large sample sizes this is likely due to a lack of persistence in specular reflections over the large angular intervals required to obtain large samples.

Citation Download Citation

Michael D. DeVore, Joseph A. O'Sullivan, and Lee J. Montagnino "Conditionally gamma- and k-distribution models for SAR ATR", Proc. SPIE 4727, Algorithms for Synthetic Aperture Radar Imagery IX, (1 August 2002); https://doi.org/10.1117/12.478690

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available