20 May 2011 Persistent SAR change detection with posterior models
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This paper develops a hierarchical Bayes model for multiple-pass, multiple antenna synthetic aperture radar (SAR) systems with the goal of adaptive change detection. The model is based on decomposing the observed data into a low-rank component and a sparse component, similar to Robust Principal Component Analysis, previously developed by Ding, He, and Carin1 for E/O systems. The developed model also accounts for SAR phenomenology, including antenna and spatial dependencies, speckle and specular noise, and stationary clutter. Monte Carlo methods are used to estimate the posterior distribution of the variables in the model. The performance of the proposed method is analyzed using synthetic images, and it is shown that the performance is robust to a large space of operating characteristics without extensive tuning of hyperparameters. Finally, the method is applied to measured SAR data, providing competitive results compared to standard methods with the additional benefits of uncertainty characterization through a posterior distribution, explicit estimates of both foreground and background components, and flexibility in including other sources of information.
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Gregory E. Newstadt, Gregory E. Newstadt, Edmund G. Zelnio, Edmund G. Zelnio, Alfred O. Hero, Alfred O. Hero, "Persistent SAR change detection with posterior models", Proc. SPIE 8051, Algorithms for Synthetic Aperture Radar Imagery XVIII, 80510R (20 May 2011); doi: 10.1117/12.888956; https://doi.org/10.1117/12.888956

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