Mr. Richard Borth Profile

Richard Borth

at US Air Force

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Proceedings Article | 7 June 2024 Presentation + Paper

Synthetic datasets for microwave dish mensuration via pose estimation in deep learning models

Christopher Liberatore, Corey Marrs, John Bielas, Amanda Baxter, Richard Borth, Ian Matejka, Yuki Adams, Patrick Benasutti, Rachel Kinard

Proceedings Volume 13035, 130350G (2024) https://doi.org/10.1117/12.3013436

KEYWORDS: Image segmentation, Deep learning, Data modeling, Pose estimation, Microwave radiation

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We present an application of synthetic datasets to a pose estimation problem called “Microwave Dish Mensuration”. Dish mensuration is the task of determining a microwave dish pointing angle from photogrammetry. Pose estimation presents a difficult case for machine learning, as it is onerous to collect a measured dataset capturing all possible configurations of an object or collection of objects; however, the ease of generating synthetic data may make the pose estimation problem tractable. Dish Mensuration has an additional benefit of having a well-known geometric invariance: a circular outline of a microwave dish, when rotated in 3D space, projects to an ellipse, and from the parameters of the ellipse, the 3D rotation relative to the sensor can be inferred. It is hoped that this geometric invariance will help the synthetic training regime generalize to measured data, and moreover, present a path forward to generalized models trained on synthetic datasets. For this research, we generated a dataset of 86,400 images of 5 different Microwave Dish models taken at 6 different times of day, generating both rendered image chips and component masks, facilitating pose estimation. We discuss the methods for generating the synthetic dataset, difficulties associated with generating sufficient variance, and a method for performing dish mensuration with a Deep Learning regression model. We conclude by addressing next steps and ways to further generalize into more pose estimation problems.

Proceedings Article | 13 June 2023 Presentation + Paper

SATEN: SAR adversarial attack using targets to evaluate neural networks

Alexander Doom, Christopher Szul, Richard Borth

Proceedings Volume 12520, 125200F (2023) https://doi.org/10.1117/12.2665983

KEYWORDS: Data modeling, Synthetic aperture radar, Image classification, Sensors, Machine learning

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Synthetic Aperture Radar (SAR) imaging provides useful remote sensing capabilities because of its ability to image day-or-night and through clouds by using radar waves. However, understanding SAR vulnerabilities is important in developing data exploitation techniques that are resistant to “spoofing.” “Spoofing” is a type of attack where a virtual object is created in a SAR image by coherently adding the expected radar returns from a target into radar returns from the background. This research explored the effects of spoofing on Convolutional Neural Network (CNN) models for vehicle classification from the SAMPLE V2 data set. CNN models trained on SAR images with real targets in the scene were not able to accurately generalize to images with virtual targets in the scene; however, a model trained on real data identified spoofed images with an accuracy over 95.0% based on the confidence value outputs and a known proportion of spoofed images. Furthermore, a specialized training methodology enabled a CNN model to classify images as real or spoofed with an accuracy of more than 99.9% and classify the vehicle type with an accuracy of over 99.5%. This research determined the effects of real and spoofed SAR images on CNN models and what methods could be leverage to improve model performance.

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