Ms. Elizabeth R. Sudkamp Profile

Elizabeth R. Sudkamp

at Air Force Research Lab

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Author

Publications (5)

Proceedings Article | 27 April 2020 Presentation

Using bayes error rate estimation to analyze feature spaces (Conference Presentation)

Sriram Bapatla, Edmund Zelnio, Elizabeth Sudkamp

Proceedings Volume 11393, 113930R (2020) https://doi.org/10.1117/12.2561773

KEYWORDS: Error analysis, Statistical analysis, Synthetic aperture radar, 3D modeling, Solid modeling, Computer aided design, Neural networks, Matrices, Visualization, 3D acquisition

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This study wishes to apply a non-parametric estimate of the Bayes Error Rate (BER) to current Air Force problems related to target classification. Whether they be neural networks, autoencoders, or other architectures, classifiers are commonly assessed through confusion matrices and associated statistics, or visualizations of feature spaces like t-SNE plots. However, these methods depend on the test data used to assess the performance of the network, not the robustness of the classifier itself. This research incorporates a different statistic that estimates the BER, or the probability of misclassification given some data, to serve as an upper bound for potential classifier performance. This estimate leverages a Friedman-Rafsky test statistic: the number of cross labels in a minimum-spanning tree (MST) through points in the feature space. The first part of this study examines the behavior of the BER estimate over a general learning process, such as different epochs of the training process in a neural network. The second part of the study examines whether certain factors affect the separability of synthetic aperture radar (SAR) images of targets of interest. Given the fact that it is often difficult and expensive to survey real targets and generate SAR images, 3-D CAD models are frequently used to generate synthetic SAR images. However, given that many resources are devoted to perfecting these models, this study applies the BER estimate to examine whether minute changes to CAD models affect separability in the image domain. The results seem to indicate that, if the topology of the target is maintained in the CAD domain, low-fidelity versions of targets (with 25% the number of faces of highly accurate models), exhibit separability and ability to be correctly classified identical to their high-fidelity counterparts. The BER estimation also shows promising applications in other domains, serving as a way to describe the underlying structure of feature spaces intuitively but effectively.

Proceedings Article | 14 May 2019 Presentation + Paper

A SAR dataset for ATR development: the Synthetic and Measured Paired Labeled Experiment (SAMPLE)

Benjamin Lewis, Theresa Scarnati, Elizabeth Sudkamp, John Nehrbass, Stephen Rosencrantz, Edmund Zelnio

Proceedings Volume 10987, 109870H (2019) https://doi.org/10.1117/12.2523460

KEYWORDS: Synthetic aperture radar, Data modeling, Radar, Automatic target recognition, Sensors, Detection and tracking algorithms, Algorithm development

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Proceedings Article | 14 May 2019 Presentation + Paper

Synthetic data accuracy sensitivity to CAD model accuracy using ATR-based metrics

Elizabeth Sudkamp, John Nehrbass, Eric Branch, Michael Levy

Proceedings Volume 10987, 109870I (2019) https://doi.org/10.1117/12.2523456

KEYWORDS: Computer aided design, 3D modeling, Synthetic aperture radar, Visualization, Automatic target recognition, Neural networks, Evolutionary algorithms

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Proceedings Article | 9 May 2018 Paper

Deep learning model-based algorithm for SAR ATR

Robert Friedlander, Michael Levy, Elizabeth Sudkamp, Edmund Zelnio

Proceedings Volume 10647, 106470B (2018) https://doi.org/10.1117/12.2315265

KEYWORDS: Synthetic aperture radar, Automatic target recognition, Neural networks, Data modeling, Model-based design, Convolutional neural networks, Image processing, Target recognition, Image classification, Linear filtering

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Proceedings Article | 27 April 2018 Paper

"AFacet": a geometry based format and visualizer to support SAR and multisensor signature generation

Stephen Rosencrantz, John Nehrbass, Ed Zelnio, Beth Sudkamp

Proceedings Volume 10647, 1064702 (2018) https://doi.org/10.1117/12.2304460

KEYWORDS: Reverse modeling, Synthetic aperture radar, Binary data, Data modeling, Databases, 3D modeling, Computer programming, Computer programming languages, Visualization, Infrared signatures

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