MSTAR target classification using Bayesian pattern theory

Raman K. Mehra; Ravi B. Ravichandran; Anuj Srivastava

doi:10.1117/12.321869

15 September 1998 MSTAR target classification using Bayesian pattern theory

Raman K. Mehra, Ravi B. Ravichandran, Anuj Srivastava

Proceedings Volume 3370, Algorithms for Synthetic Aperture Radar Imagery V; (1998) https://doi.org/10.1117/12.321869
Event: Aerospace/Defense Sensing and Controls, 1998, Orlando, FL, United States

Abstract

In the work described herein, Bayesian Pattern Theory is used to formulate the overall ATR problem as the optimization of a single objective function over the parameters to be estimated. Thus, all image understanding operations are then realized naturally, automatically, and consistently as byproducts of a large-scale stochastic optimization process. The work begins with a derivation of the Bayesian cost function by deriving a posterior probability distribution on the space of pose parameters and solves the optimization problem with respect to this posterior. Two noise models were considered in the derivation of the cost function: the first is the commonly used Gaussian model, and the second, considering that a SAR image is complex, is a Rician model. In order to test the robustness of the algorithm with respect to target types and adverse background conditions, four cases were constructed: Case (1) Gaussian noise was used and a Gaussian noise model was used in classification. Case (2) Rician noise was used and a Gaussian noise model was used in classification, Case (3) Rician noise was used and a Rician noise model was used in classification, and Case (4) MSTAR clutter was used. For each cases, we compute the probability of detection as a function of SNR. We obtained very good results for Case (1), however, the results at very low SNR may be unrealistic because the Gaussian noise assumptions are not accurate. As expected, the results for Case (2) were poor while the results for Case (3) were good. Compared to Case (1) the Case (3) results are more reliable because of a representative Rician noise model. The results for Case (4) were also good. These results were also independently confirmed by Bayes error analysis.

Citation Download Citation

Raman K. Mehra, Ravi B. Ravichandran, and Anuj Srivastava "MSTAR target classification using Bayesian pattern theory", Proc. SPIE 3370, Algorithms for Synthetic Aperture Radar Imagery V, (15 September 1998); https://doi.org/10.1117/12.321869

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