Algae bloom intensity classification using machine learning methods and UAV hyperspectral data

This paper presents an approach for high spatial resolution hyperspectral image analysis in an applied task of river water condition assessment. The method allows the detection of algal blooms or water pollution by foreign substances. High-resolution hyperspectral images were obtained using a hyperspectrometer mounted on a small unmanned aerial vehicle. A difference between the spectra of river parts with varying intensity of algal blooms was demonstrated. Water samples were taken, and chemical analysis confirmed the varying levels of magnesium and calcium across all samples, corresponding to the intensity of algal blooms in the water. Several machine learning-based classification algorithms and vegetation indices were considered for classifying water areas with varying intensities of algal blooms. The effectiveness of machine learning algorithms compared to vegetation indices was shown. In addition, to improve the performance of the most effective classification algorithms, a comparison of several dimensionality reduction approaches based on spectral channel selection was carried out.