Machine-learning-based size estimation of marine particles in holograms recorded by a submersible digital holographic camera.

Liu, Zonghua; Giering, Sarah; Thevar, Thangavel; Burns, Nick; Ockwell, Mike; Watson, John

Abstract

Particle size estimation is key to understanding carbon fluxes and storage in the marine ecosystem. Images of particles provide much information about their size. A subsea digital holographic camera was used to image particles in vertical trajectory in South Georgia. The holograms were processed using a rapid hologram processing suite that extracted focused particle vignettes from these raw holograms. A machine-learning-based method has been developed to analyse the particle size information from these vignettes. To be specific, a structured-forest-based model trained on a group of synthetic holographic particle images is used to detect the particle edges in these vignettes. Following that, a set of pixel-wise morphology operators are used to extract particle regions (masks) from their edge images. Lastly, the size information of the recorded particles can be calculated based on these mask images. The proposed method has been evaluated on a group of synthetic holograms and real holograms, compared with the other ten methods, including four edge-based methods, four region-based methods, a thresholding-based method, and a Kmeans-based method. The results show that our method has the best performance regarding accuracy and processing time. It reaches ∼0.7 of mean IoU and ∼25 s of running time on the 1,000 test vignettes. In terms of qualitative analysis, the regions of the given examples extracted by the proposed method closely match the real particle regions. We also use this method to analyse the size distributions of two profiles, and some generic results are given in this paper.

Citation

LIU, Z., GIERING, S., THEVAR, T., BURNS, N., OCKWELL, M. and WATSON, J. 2023. Machine-learning-based size estimation of marine particles in holograms recorded by a submersible digital holographic camera. In Proceedings of OCEANS 2023 - Limerick, 5-8 June 2023, Limerick, Ireland. Piscataway: IEEE [online], article number 10244456. Available from: https://doi.org/10.1109/OCEANSLimerick52467.2023.10244456

• Presentation Conference Type: Conference Paper (published)
• Conference Name: MTS/IEEE Oceans 23
• Start Date: Jun 5, 2023
• End Date: Jun 8, 2023
• Acceptance Date: Jun 5, 2023
• Online Publication Date: Feb 20, 2023
• Publication Date: Sep 12, 2023
• Deposit Date: Feb 21, 2025
• Publicly Available Date: Feb 21, 2025
• Publisher: Institute of Electrical and Electronics Engineers (IEEE)
• Peer Reviewed: Peer Reviewed
• Article Number: 10244456
• ISBN: 9798350332261
• DOI: https://doi.org/10.1109/OCEANSLimerick52467.2023.10244456
• Keywords: Subsea digital holography; Size estimation; Particle size distributions; Hologram processing; Machine learning
• Public URL: https://rgu-repository.worktribe.com/output/2662635