Accession Number:

ADA581711

Title:

A Generalizable Hierarchical Bayesian Model for Persistent SAR Change Detection

Descriptive Note:

Conference paper

Corporate Author:

MICHIGAN UNIV ANN ARBOR DEPT OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCE

Report Date:

2012-04-01

Pagination or Media Count:

22.0

Abstract:

This paper proposes a hierarchical Bayesian model for multiple-pass, multiple antenna synthetic aperture radar SAR systems with the goal of adaptive change detection. We model the SAR phenomenology directly, including antenna and spatial dependencies, speckle and specular noise, and stationary clutter. We extend previous work by estimating the antenna covariance matrix directly, leading to improved performance in high clutter regions. The proposed SAR model also is shown to be easily generalizable when additional prior information is available, such as locations of roadsintersections or smoothness priors on the target motion. The performance of our posterior inference algorithm is analyzed over a large set of measured SAR imagery. It is shown that the proposed algorithm provides competitive or better results than common change detection algorithms with additional benefits such as few tuning parameters and a characterization of the posterior distribution.

Subject Categories:

  • Statistics and Probability
  • Active and Passive Radar Detection and Equipment
  • Target Direction, Range and Position Finding

Distribution Statement:

APPROVED FOR PUBLIC RELEASE