Model matching for SAR ATR based on probabilistic distance transforms

David M. Doria

doi:10.1117/12.276093

10 June 1997 Model matching for SAR ATR based on probabilistic distance transforms

David M. Doria

Proceedings Volume 3066, Radar Sensor Technology II; (1997) https://doi.org/10.1117/12.276093
Event: AeroSense '97, 1997, Orlando, FL, United States

Abstract

In this paper we present an analysis of distance transform methods of matching object models to SAR data. We show that by properly defining the distance function, the likelihood of each observed SAR feature data point given the model is given as a function of position. This allows calculation of a likelihood of observing a set of data features, given a model and its associated pose and other parameters. The issue of normalization resulting from the non-correspondence based distance transform method is discussed. When prior densities of the model features are available, maximum a- posteriori results are obtainable. This method allows the use of priors of models and individual features, along with the geometric probability densities associated with the feature prediction and measurement processes, to be incorporated within a fast correlation-type distance transform matching module. The method also potentially allows exploitation of persistent scatterers over a limited range of SAR model-to-target imaging parameters.

Citation Download Citation

David M. Doria "Model matching for SAR ATR based on probabilistic distance transforms", Proc. SPIE 3066, Radar Sensor Technology II, (10 June 1997); https://doi.org/10.1117/12.276093

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