Dr. Sophia P. Bragdon Profile

Dr. Sophia P. Bragdon

at US Army Engineer Research and Development Ctr

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Proceedings Article | 14 June 2023 Poster + Paper

Comparing a Bayesian classifier for acoustic signals to machine-learning classifiers

Sophia Bragdon, D. Keith Wilson, Chris Pettit

Proceedings Volume 12547, 1254711 (2023) https://doi.org/10.1117/12.2663959

KEYWORDS: Acoustics, Machine learning, Image classification, Automatic target recognition, Scattering

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Acoustic signals in complex real-world environments are randomized by processes such as multipath reflections, surface scattering, and volume scattering by turbulence and vegetation. Traditional machine learning classification algorithms require large datasets with substantial variation to mitigate the challenge posed by randomized signals. In this paper, a Bayesian classifier is introduced that incorporates the underlying physics of the scattered signal into a realistic likelihood distribution for the random signal variations. Performance of the Bayesian classifier is compared to machine learning classification algorithms based on two convolutional neural networks (CNN), one for images and one for sound. Given the varying architectures of the three classification algorithms each approach requires a different feature set. The image-based CNN uses a spectrogram as the input, the soundbased CNN uses the normalized waveform, and the Bayesian classifier is developed on features given by the power in the one-third octave bands. For the comparison, we employ the acoustic seismic classification identification data set (ACIDS) and the environmental sound classification data set (ESC-50). To account for the small size of the considered data sets, we use transfer learning for the machine learning classification algorithms. For the ACIDS and ESC-50 data sets, we show that the Bayesian classifier method outperforms the image-based CNN and the sound-based CNN when measured by accuracy on a test set of data. Unlike the physics-based Bayesian classifier, the machine learning algorithms do not take advantage of the underlying physics of the acoustic signal, which impacts the classification accuracy of traditional machine learning approaches.

Proceedings Article | 13 June 2023 Presentation + Paper

Leveraging environmental conditions to inform a two-step ATR for buried objects

Sophia Potoczak Bragdon, Vuong Truong, Megan Bishop, Jay Clausen

Proceedings Volume 12521, 125210M (2023) https://doi.org/10.1117/12.2663970

KEYWORDS: Long wavelength infrared, Object detection, Image classification, Environmental sensing, Automatic target recognition, Neural networks, Image filtering

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The detection and classification of buried objects utilizing long wave infrared (LWIR) imaging is a challenging task. The ability to detect a buried object is reliant on discriminating background noise from surface temperature anomalies induced by the presence of a foreign object below ground surface. The presence of background noise and temperature anomalies in LWIR images containing buried objects is correlated to the ambient environmental conditions. For example, increased solar loading of the soil can lead to increased background noise, while increased volumetric water content of the soil can mask the presence of temperature anomalies due to buried objects. This paper discusses advancements to a proposed environmentally informed two-step automatic target recognition (ATR) algorithm for buried objects and the characterization of environmental phenomenology corresponding to buried objects and background noise. The detection step of the algorithm is based on an edge detection approach and is designed to maximize probability of detection while ignoring the false alarm rate. The classification step filters the false alarms from the true alarms utilizing a novel framework that combines the environmental conditions with the LWIR imagery. The environmentally informed classification algorithm concurrently reasons from a set of environmental conditions recorded by sensors coupled with a region of interest detected in the first step. The classification algorithm combines a CNNbased image machine learning algorithm with a fully connected neural network to extract features on the coupled environmental and image data to ultimately produce a classification. The performance of the algorithm is compared to common machine learning ATRs.

Proceedings Article | 30 May 2022 Presentation + Paper

Environmentally informed buried object recognition

Sophia Potoczak Bragdon, Vuong Truong, Jay Clausen

Proceedings Volume 12116, 1211610 (2022) https://doi.org/10.1117/12.2616583

KEYWORDS: Thermography, Detection and tracking algorithms, Image classification, Image processing, Image filtering, Signal to noise ratio, Edge detection, Sensors, Environmental sensing, Machine learning

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The ability to detect and classify buried objects using thermal infrared imaging is affected by the environmental conditions at the time of imaging, which leads to an inconsistent probability of detection. For example, periods of dense overcast or recent precipitation events result in the suppression of the soil temperature difference between the buried object and soil, thus preventing detection. This work introduces an environmentally informed framework to reduce the false alarm rate in the classification of regions of interest (ROIs) in thermal IR images containing buried objects. Using a dataset that consists of thermal images containing buried objects paired with the corresponding environmental and meteorological conditions, we employ a machine learning approach to determine which environmental conditions are the most impactful on the visibility of the buried objects. We find the key environmental conditions include incoming short-wave solar radiation, soil volumetric water content, and average air temperature. For each image, ROIs are computed using a computer vision approach and these ROIs are coupled with the most important environmental conditions to form the input for the classification algorithm. The environmentally informed classification algorithm produces a decision on whether the ROI contains a buried object by simultaneously learning on the ROIs with a classification neural network and on the environmental data using a tabular neural network. On a given set of ROIs, we have shown that the environmentally informed classification approach improves the detection of buried objects within the ROIs.

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