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dc.contributor.authorDavies, EJen
dc.contributor.authorBuscombe, Den
dc.contributor.authorGraham, GWen
dc.contributor.authorNimmo-Smith, WAMen

© 2015 American Meteorological Society. Substantial information can be gained from digital in-line holography of marine particles, eliminating depth-of-field and focusing errors associated with standard lens-based imagingmethods.However, for the technique to reach its full potential in oceanographic research, fully unsupervised (automated) methods are required for focusing, segmentation, sizing, and classification of particles. These computational challenges are the subject of this paper, in which the authors draw upon data collected using a variety of holographic systems developed at Plymouth University, United Kingdom, from a significant range of particle types, sizes, and shapes. A new method for noise reduction in reconstructed planes is found to be successful in aiding particle segmentation and sizing. The performance of an automated routine for deriving particle characteristics (and subsequent size distributions) is evaluated against equivalent size metrics obtained by a trained operative measuring grain axes on screen. The unsupervised method is found to be reliable, despite some errors resulting from oversegmentation of particles. A simple unsupervised particle classification system is developed and is capable of successfully differentiating sand grains, bubbles, and diatoms from within the surfzone. Avoiding miscounting bubbles and biological particles as sand grains enables more accurate estimates of sand concentrations and is especially important in deployments of particle monitoring instrumentation in aerated water. Perhaps the greatest potential for further development in the computational aspects of particle holography is in the area of unsupervised particle classification. The simple method proposed here provides a foundation upon which further development could lead to reliable identification of more complex particle populations, such as those containing phytoplankton, zooplankton, flocculated cohesive sediments, and oil droplets.

dc.format.extent1241 - 1256en
dc.titleEvaluating unsupervised methods to size and classify suspended particles using digital in-line holographyen
dc.typeJournal Article
plymouth.journalJournal of Atmospheric and Oceanic Technologyen
plymouth.organisational-group/Plymouth/00 Groups by role
plymouth.organisational-group/Plymouth/00 Groups by role/Academics
plymouth.organisational-group/Plymouth/Faculty of Science and Engineering
plymouth.organisational-group/Plymouth/Faculty of Science and Engineering/School of Biological and Marine Sciences
plymouth.organisational-group/Plymouth/PRIMaRE Publications
plymouth.organisational-group/Plymouth/REF 2021 Researchers by UoA
plymouth.organisational-group/Plymouth/REF 2021 Researchers by UoA/UoA07 Earth Systems and Environmental Sciences
plymouth.organisational-group/Plymouth/Research Groups
plymouth.organisational-group/Plymouth/Research Groups/Marine Institute
dc.rights.embargoperiodNo embargoen
rioxxterms.typeJournal Article/Reviewen

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