Journal of Undergraduate Research
Keywords
Bayesian nonparametric, statistical model, hyperspectral data analysis
College
Physical and Mathematical Sciences
Department
Physics and Astronomy
Abstract
Hyperspectral imaging (HSI) is a technology that provides a dense set of previously un- available data{o ering the opportunity for use in a variety of applications such as food safety, ecology, and non-proliferation research. HSI stores measurements across three dimen- sions (two-dimensional space and the electromagnetic spectrum), resulting in large, three- dimensional data cubes. This additional amount of information can be used to identify materials in the images remotely. However, due to the many possibilities for measurement and other errors, it is hard to distinguish between the signal (the material spectra) and these sources of noise. We combine a known physical model within a statistical model to separate out the material-specic emissivity spectra to better identify materials in these images. Specically, we designed a Bayesian nonparametric statistical model, and tested its eciency using simulation studies.
Recommended Citation
Seeger, Jessica and Berrett, Dr. Candace
(2014)
"A Bayesian Nonparametric Approach to Hyperspectral Data Analysis,"
Journal of Undergraduate Research: Vol. 2014:
Iss.
1, Article 1283.
Available at:
https://scholarsarchive.byu.edu/jur/vol2014/iss1/1283