Hyperspectral imaging and analysis of mixed algae species in liquid media
Mehrübe Mehrübeoğlu, Ming Yang Teng, Marci Savage, Alexandra Rafalski, Paul V. Zimba
Abstract
Mehrübe Mehrübeoğlu, Ming Yang Teng, Marci Savage, Alexandra Rafalski, Paul V. Zimba
Abstract
In this paper, a laboratory-based hyperspectral imaging system is used to acquire hyperspectral data cubes from different algae samples of known mixtures. The data are obtained under controlled and repeatable conditions. Hyperspectral image processing is complicated by the size of the corresponding datasets so hyperspectral image pre-processing techniques such as dimensionality reduction are necessary before spectral analysis. We assessed hyperspectral response of mixed algal cultures containing two algae types to characterize the laboratory-based hyperspectral imaging system. Changes in the hyper spectral imaging system's response to variations in volume and combinations of algae concentrations were tested. Preliminary results demonstrate the system's capability to differentiate algal species, concentrations and sample volumes.
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In this paper, a laboratory-based hyperspectral imaging system is used to acquire hyperspectral data cubes from different algae samples of known mixtures. The data are obtained under controlled and repeatable conditions. Hyperspectral image processing is complicated by the size of the corresponding datasets so hyperspectral image pre-processing techniques such as dimensionality reduction are necessary before spectral analysis. We assessed hyperspectral response of mixed algal cultures containing two algae types to characterize the laboratory-based hyperspectral imaging system. Changes in the hyper spectral imaging system's response to variations in volume and combinations of algae concentrations were tested. Preliminary results demonstrate the system's capability to differentiate algal species, concentrations and sample volumes.
Key concepts: Hyperspectral imaging, Algae, Remote sensing, Image processing, Environmental science, Dimensionality reduction, Computer science, Biological system