PlumeNET: a convolutional neural network for plume classification in thermal imagery

Christian W. Smith; Julia R. Dupuis; William J. Marinelli

doi:10.1117/12.2518763

13 May 2019 PlumeNET: a convolutional neural network for plume classification in thermal imagery

Christian W. Smith, Julia R. Dupuis, William J. Marinelli

Proceedings Volume 10990, Computational Imaging IV; 109900L (2019) https://doi.org/10.1117/12.2518763
Event: SPIE Defense + Commercial Sensing, 2019, Baltimore, MD, United States

Abstract

The development of PlumeNet, a thermal imagery based classifier for aerosolized chemical and biological warfare agents, is detailed. PlumeNet is a convolutional neural network designed for the real-time classification of threat-like plumes from background clutter. The model weights were trained from the ground up using thermal imagery of simulant plumes recorded at various test events. The performance between different convolutional neural network architectures are compared. An analysis of the final model layers through activation mapping methods is performed to demystify the methods by which PlumeNet performs classification. The classification performance of PlumeNet at government conducted open-release field testing at Dugway Proving Ground is detailed.

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Christian W. Smith, Julia R. Dupuis, and William J. Marinelli "PlumeNET: a convolutional neural network for plume classification in thermal imagery", Proc. SPIE 10990, Computational Imaging IV, 109900L (13 May 2019); https://doi.org/10.1117/12.2518763

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KEYWORDS

Data modeling

Thermography

Atmospheric modeling

Thermal modeling

Image classification

Performance modeling

Convolutional neural networks

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