Mapping pigment distribution in mud samples through hyperspectral imaging

Mehrube Mehrübeoglu; Cosmina Nicula; Christopher Trombley; Shane W. Smith; Dustin K. Smith; Elizabeth S. Shanks; Paul V. Zimba

doi:10.1117/12.2190059

10 November 2015 Mapping pigment distribution in mud samples through hyperspectral imaging

Mehrube Mehrübeoglu, Cosmina Nicula, Christopher Trombley, Shane W. Smith, Dustin K. Smith, Elizabeth S. Shanks, Paul V. Zimba

Author Affiliations +

Proceedings Volume 9611, Imaging Spectrometry XX; 961106 (2015) https://doi.org/10.1117/12.2190059
Event: SPIE Optical Engineering + Applications, 2015, San Diego, California, United States

Abstract

Mud samples collected from bodies of water reveal information about the distribution of microorganisms in the local sediments. Hyperspectral imaging has been investigated as a technology to identify phototropic organisms living on sediments collected from the Texas Coastal Bend area based on their spectral pigment profiles and spatial arrangement. The top pigment profiles identified through high-performance liquid chromatography (HPLC) have been correlated with spectral signatures extracted from the hyperspectral data of mud using fast Fourier transform (FFT). Spatial distributions have also been investigated using 2D hyperspectral image processing. 2D pigment distribution maps have been created based on the correlation with pigment profiles in the FFT domain. Among the tested pigments, the results show match among four out of five pigment distribution trends between HPLC and hyperspectral data analysis. Differences are attributed mainly to the difference between area and volume of scale between the HPLC analysis and area covered by hyperspectral imaging.

Citation Download Citation

Mehrube Mehrübeoglu, Cosmina Nicula, Christopher Trombley, Shane W. Smith, Dustin K. Smith, Elizabeth S. Shanks, and Paul V. Zimba "Mapping pigment distribution in mud samples through hyperspectral imaging", Proc. SPIE 9611, Imaging Spectrometry XX, 961106 (10 November 2015); https://doi.org/10.1117/12.2190059

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KEYWORDS

Hyperspectral imaging

Data modeling

Fourier transforms

Image processing

Reflectivity

Absorbance

Image analysis

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