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Resolving Mixed Algal Species in Hyperspectral Images
AbstractWe investigated a lab-based hyperspectral imaging system’s response from pure (single) and mixed (two) algal cultures containing known algae types and volumetric combinations to characterize the system’s performance. The spectral response to volumetric changes in single and combinations of algal mixtures with known ratios were tested. Constrained linear spectral unmixing was applied to extract the algal content of the mixtures based on abundances that produced the lowest root mean square error. Percent prediction error was computed as the difference between actual percent volumetric content and abundances at minimum RMS error. Best prediction errors were computed as 0.4%, 0.4% and 6.3% for the mixed spectra from three independent experiments. The worst prediction errors were found as 5.6%, 5.4% and 13.4% for the same order of experiments. Additionally, Beer-Lambert’s law was utilized to relate transmittance to different volumes of pure algal suspensions demonstrating linear logarithmic trends for optical property measurements.
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Mehrubeoglu, M.; Teng, M.Y.; Zimba, P.V. Resolving Mixed Algal Species in Hyperspectral Images. Sensors 2014, 14, 1-21.View more citation formats
Mehrubeoglu M, Teng MY, Zimba PV. Resolving Mixed Algal Species in Hyperspectral Images. Sensors. 2014; 14(1):1-21.Chicago/Turabian Style
Mehrubeoglu, Mehrube; Teng, Ming Y.; Zimba, Paul V. 2014. "Resolving Mixed Algal Species in Hyperspectral Images." Sensors 14, no. 1: 1-21.