Feature extraction using attributed scattering center models on SAR imagery

Michael A. Koets; Randolph L. Moses

doi:10.1117/12.357628

13 August 1999 Feature extraction using attributed scattering center models on SAR imagery

Michael A. Koets, Randolph L. Moses

Proceedings Volume 3721, Algorithms for Synthetic Aperture Radar Imagery VI; (1999) https://doi.org/10.1117/12.357628
Event: AeroSense '99, 1999, Orlando, FL, United States

Abstract

We present algorithms for feature extraction from complex SAR imagery. The features parameterize an attributed scattering center model that describes both frequency and aspect dependence of scattering centers on the target. The scattering attributes extend the widely-used point scattering model, and characterize physical properties of the scattering object. We present two feature extraction algorithms, an approximate maximum likelihood method that relies on minimization of a nonlinear cost function, and a computationally faster method that avoids the nonlinear minimization step. We present results of applying both algorithms on synthetic model data, on XPatch scattering predictions of the SLICY test target, and on measured X-band SAR imagery.

Citation Download Citation

Michael A. Koets and Randolph L. Moses "Feature extraction using attributed scattering center models on SAR imagery", Proc. SPIE 3721, Algorithms for Synthetic Aperture Radar Imagery VI, (13 August 1999); https://doi.org/10.1117/12.357628

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