Identification of microalgal particles using pixel feature analysis of Mueller matrix images

Abstract

Fine sorting of aquatic particles is of great significance for water environment monitoring. Natural water contains aquatic particles that exhibit a high degree of diversity and complexity, and the identification of aquatic particles remains a persistent challenge in the field. In this article, we propose a new technique for identifying the target species of microalgal particles by using the pixel feature analysis of Mueller matrix images. This technique is independent of any prior knowledge or data about the existing particles in the environment, which is advantageous when applied to real-world situations. The pixel-level polarimetric features are fully leveraged to construct polarization feature templates, which can be used to characterize and filter specific microalgal particles in complex environments. This method could enable the accurate detection of harmful algal blooms species in natural water, which can facilitate early warning of algal blooms. The preliminary results show that the recall rate reached 97.2%, and the average accuracy is 98.9%, which demonstrate the effectiveness of this approach for identifying the target species of aquatic particles in natural water.