A Generalizable Hierarchical Bayesian Model for Persistent SAR Change Detection

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 roads/intersections 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.

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Document Details

Document Type
Technical Report
Publication Date
Apr 01, 2012
Accession Number
ADA581711

Entities

People

  • Alfred O. Hero III
  • Edmund G. Zelnio
  • Gregory E. Newstadt

Organizations

  • University of Michigan

Tags

DTIC Thesaurus Topics

  • Algorithms
  • Bayesian Networks
  • Change Detection
  • Computational Science
  • Computer Science
  • Covariance
  • Detection
  • Detectors
  • Electrical Engineering
  • False Alarms
  • Hidden Markov Models
  • Models
  • Monte Carlo Method
  • Probability
  • Radar
  • Synthetic Aperture Radar
  • Target Signatures

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Neural Network Machine Learning.
  • Radar Systems Engineering.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - Machine Learning Algorithms