Increasing the discrimination of synthetic aperture radar recognition models

Bir Bhanu; Grinnell Jones III

doi:10.1117/1.1517286

1 December 2002 Increasing the discrimination of synthetic aperture radar recognition models

Bir Bhanu, Grinnell Jones III

Optical Engineering, Vol. 41, Issue 12, (December 2002). https://doi.org/10.1117/1.1517286

The focus of this work is optimizing recognition models for synthetic aperture radar (SAR) signatures of vehicles to improve the performance of a recognition algorithm under the extended operating conditions of target articulation, occlusion, and configuration variants. The recognition models are based on quasi-invariant local features, scattering center locations, and magnitudes. The approach determines the similarities and differences among the various vehicle models. Methods to penalize similar features or reward dissimilar features are used to increase the distinguishability of the recognition model instances. Extensive experimental recognition results are presented in terms of confusion matrices and receiver operating characteristic (ROC) curves to show the improvements in recognition performance for real SAR signatures of vehicle targets with articulation, configuration variants, and occlusion.

©(2002) Society of Photo-Optical Instrumentation Engineers (SPIE)

Citation Download Citation

Bir Bhanu and Grinnell Jones III "Increasing the discrimination of synthetic aperture radar recognition models," Optical Engineering 41(12), (1 December 2002). https://doi.org/10.1117/1.1517286

Published: 1 December 2002

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