Compact-Morphology-based poly-metallic Nodule Delineation.

Schoening, Timm, Jones, Daniel and Greinert, Jens (2017) Compact-Morphology-based poly-metallic Nodule Delineation. Open Access Scientific Reports, 7 (13338 ). DOI 10.1038/s41598-017-13335-x.

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Abstract

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.

Document Type: Article
Keywords: Poly-metallic nodules, manganese nodules, image processing, deep sea mining
Research affiliation: OceanRep > GEOMAR > FB2 Marine Biogeochemistry > FB2-MG Marine Geosystems
OceanRep > GEOMAR > FB2 Marine Biogeochemistry > FB2-MG Marine Geosystems > DeepSea Monitoring
Kiel University
Refereed: Yes
Open Access Journal?: Yes
DOI etc.: 10.1038/s41598-017-13335-x
ISSN: 2045-2322
Projects: JPIO-MiningImpact
Contribution Number:
ProjectNumber
DSM29
Expeditions/Models/Experiments:
Date Deposited: 23 Oct 2017 12:43
Last Modified: 02 Mar 2018 08:27
URI: http://oceanrep.geomar.de/id/eprint/39926

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