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11 March 2024 Performance evaluation of supervised classifications for detecting trace explosives using multispectral camera
Daniel Burrow, Wenli Huang
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Proceedings Volume 12893, Photonic Instrumentation Engineering XI; 128930T (2024) https://doi.org/10.1117/12.2692717
Event: SPIE OPTO, 2024, San Francisco, California, United States
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Abstract
This paper aims to evaluate the performance of several supervised machine learning methods as to determine the best algorithm for detecting explosives with multispectral imagery. Ocean Thin Films SpectroCam with 8 interchangeable band pass filters is used to collect images. The stack of 8-dimensional data cube can be obtained and subsequently analyzed with various machine learning algorithms. We specifically study four classifiers: Convolutional Neural Network, Support Vector Machine, Quadratic Discriminant Analysis, and Linear Discriminant Analysis. We examine and compare the accuracy of the four classifiers’ performance in the application of detecting trace C4 material. Our results show that the Support Vector Machine and Convolutional Neural Network classifiers achieve the best overall accuracy, although they have the longest training time.
(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.
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Daniel Burrow and Wenli Huang "Performance evaluation of supervised classifications for detecting trace explosives using multispectral camera", Proc. SPIE 12893, Photonic Instrumentation Engineering XI, 128930T (11 March 2024); https://doi.org/10.1117/12.2692717
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KEYWORDS
Scene classification
Biological samples
Explosives
Chemical analysis
Machine learning
Support vector machines
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