nerc.ac.uk

Compact-morphology-based poly-metallic nodule delineation

Schoening, Timm; Jones, Daniel O.B.; Greinert, Jens. 2017 Compact-morphology-based poly-metallic nodule delineation. Scientific Reports, 7 (1). 13338. https://doi.org/10.1038/s41598-017-13335-x

Before downloading, please read NORA policies.
[img]
Preview
Text (Open Access paper)
s41598-017-13335-x.pdf - Published Version
Available under License Creative Commons Attribution 4.0.

Download (5MB) | Preview

Abstract/Summary

Poly-metallic nodules are a marine resource considered for deep sea mining. Assessing nodule abundance is of interest for mining companies and to monitor potential environmental impact. Optical seafloor imaging allows quantifying poly-metallic nodule abundance at spatial scales from centimetres to square kilometres. Towed cameras and diving robots acquire high-resolution imagery that allow detecting individual nodules and measure their sizes. Spatial abundance statistics can be computed from these size measurements, providing e.g. seafloor coverage in percent and the nodule size distribution. Detecting nodules requires segmentation of nodule pixels from pixels showing sediment background. Semi-supervised pattern recognition has been proposed to automate this task. Existing nodule segmentation algorithms employ machine learning that trains a classifier to segment the nodules in a high-dimensional feature space. Here, a rapid nodule segmentation algorithm is presented. It omits computation-intense feature-based classification and employs image processing only. It exploits a nodule compactness heuristic to delineate individual nodules. Complex machine learning methods are avoided to keep the algorithm simple and fast. The algorithm has successfully been applied to different image datasets. These data sets were acquired by different cameras, camera platforms and in varying illumination conditions. Their successful analysis shows the broad applicability of the proposed method.

Item Type: Publication - Article
Digital Object Identifier (DOI): https://doi.org/10.1038/s41598-017-13335-x
ISSN: 2045-2322
Date made live: 31 Oct 2017 11:37 +0 (UTC)
URI: http://nora.nerc.ac.uk/id/eprint/518173

Actions (login required)

View Item View Item

Document Downloads

Downloads for past 30 days

Downloads per month over past year

More statistics for this item...