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Multivariate analysis and visualization of hyperspectral data
The student will familiarize him- or herself with a number of multivariate image
reconstruction methods for the visualization and explorative analysis of hyperspectral
data, and investigate the applicability of said methods to pre-existing datasets obtained at the Photonics Laboratory from different samples of two-dimensional materials
using Raman- and photoluminescence spectroscopy.
In hyperspectral imaging a sample is raster-scanned through the focal point of a laser beam and spectroscopic information is collected pixel by pixel. The resulting spectral hypercubes [Fig. 1(a)] contain thousands of individual spectra as well as the spatial coordinates associated with each spectrum. A univariate visual representation of such a dataset can be obtained simply by color-coding a specific spectral property (e.g., the intensity or position of a certain spectral band) and displaying this color for each pair of spatial coordinates in a map. However, potentially more information can be extracted from a spectral hypercube by employing multivariate methods using entire spectra, or large parts thereof, for image reconstruction [Fig. 1(b-e)]. This includes unsupervised learning methods such as dimension reduction techniques and cluster analysis. [2, 3]
The student will 1) familiarize him- or herself with a number of multivariate image reconstruction methods for the visualization and explorative analysis of hyperspectral data, and 2) investigate the applicability of said methods to pre-existing datasets obtained at the Photonics Laboratory from different samples of two-dimensional materials
using Raman- and photoluminescence spectroscopy.
References:
[1] https://hypers.readthedocs.io/en/latest/?badge=latest
[2] Gautam et al., EPJ Techniques and Instrumentation 2, 8 (2015)
[2] Miljkovi´c et al., Analyst 135, 8 (2010)
Prerequisites:
Experience or strong interest in data analysis (preferably in python).
In hyperspectral imaging a sample is raster-scanned through the focal point of a laser beam and spectroscopic information is collected pixel by pixel. The resulting spectral hypercubes [Fig. 1(a)] contain thousands of individual spectra as well as the spatial coordinates associated with each spectrum. A univariate visual representation of such a dataset can be obtained simply by color-coding a specific spectral property (e.g., the intensity or position of a certain spectral band) and displaying this color for each pair of spatial coordinates in a map. However, potentially more information can be extracted from a spectral hypercube by employing multivariate methods using entire spectra, or large parts thereof, for image reconstruction [Fig. 1(b-e)]. This includes unsupervised learning methods such as dimension reduction techniques and cluster analysis. [2, 3] The student will 1) familiarize him- or herself with a number of multivariate image reconstruction methods for the visualization and explorative analysis of hyperspectral data, and 2) investigate the applicability of said methods to pre-existing datasets obtained at the Photonics Laboratory from different samples of two-dimensional materials using Raman- and photoluminescence spectroscopy.
References: [1] https://hypers.readthedocs.io/en/latest/?badge=latest [2] Gautam et al., EPJ Techniques and Instrumentation 2, 8 (2015) [2] Miljkovi´c et al., Analyst 135, 8 (2010)
Prerequisites: Experience or strong interest in data analysis (preferably in python).