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.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.