Share Email Print

Proceedings Paper

Probabilistic optimization approach to SAR feature matching
Author(s): Gil J. Ettinger; Gregory A. Klanderman; William M. Wells III; W. Eric L. Grimson
Format Member Price Non-Member Price
PDF $17.00 $21.00

Paper Abstract

Applying model-based vision techniques to SAR data is particularly challenging because of the inherent difficulty in generating accurate predictions of an electromagnetic signature and the variation of observed signatures to small changes in sensing conditions, imaging geometry, and object characteristics. In order to cope with these difficulties we are developing a robust feature matching model to be part of the moving and stationary target acquisition and recognition model-based automatic target recognition system. The goals of this matching module are: (1) generate correspondences between predicted features and features extracted from a SAR image, (2) evaluate the match based on the degree of uncertainty of the features and their degree of match, (3) refine the target position/orientation/articulation based on the feature correspondences, and (4) analyze residual mix- matches for cueing scene interpretations of unexplained image features. We are developing a probabilistic optimization matching approach based on a (1) Bayesian evaluation metric and (2) they dynamic solution of the best correspondences during the search of pose space. The system is designed to support a wide range of features (points, regions, and other composite features) in a wide range of situations, such as obscuration, attenuation, layover, and variable target articulations and configurations. Initial test results in these types of situations are presented.

Paper Details

Date Published: 10 June 1996
PDF: 12 pages
Proc. SPIE 2757, Algorithms for Synthetic Aperture Radar Imagery III, (10 June 1996); doi: 10.1117/12.242044
Show Author Affiliations
Gil J. Ettinger, TASC Inc. (United States)
Gregory A. Klanderman, TASC Inc. (United States)
William M. Wells III, MIT Artificial Intelligence Lab. (United States)
W. Eric L. Grimson, MIT Artificial Intelligence Lab. (United States)

Published in SPIE Proceedings Vol. 2757:
Algorithms for Synthetic Aperture Radar Imagery III
Edmund G. Zelnio; Robert J. Douglass, Editor(s)

© SPIE. Terms of Use
Back to Top
Sign in to read the full article
Create a free SPIE account to get access to
premium articles and original research
Forgot your username?