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  • 1
    Online Resource
    Online Resource
    RV POSEIDON Fahrtbericht / Cruise Report POS526 - SeASOM: Semi-Autonomous Subsurface Optical Monitoring for methane seepage and cold-water coral studies in the North Sea : Bergen (Norway) – Doggerbank (Netherlands) – Hirtshals (Denmark) – Tisler (Norway) – Kiel (Germany), 23.07. – 11.08.2018 (2019)
    Kiel : GEOMAR, Helmholtz-Zentrum für Ozeanforschung
    Details Availability
    Type of Medium: Online Resource
    Pages: 86 Seiten
    Series Statement: GEOMAR Report N. Ser. 51
    URL: https://oceanrep.geomar.de/47036/
    Language: English
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  • 2
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    Understanding Mn-nodule distribution and evaluation of related deep-sea mining impacts using AUV-based hydroacoustic and optical data (2018)
    Copernicus Publications (EGU)
    In:  Biogeosciences (BG), 15 (8). pp. 2525-2549.
    Details
    Publication Date: 2021-03-18
    Description: In this study ship- and AUV-based multibeam data from the German Mn-nodule license area in the Clarion-Clipperton Zone (CCZ; eastern Pacific) are linked to ground truth data from optical imaging. Photographs obtained by an AUV enable semi-quantitative assessments of nodule coverage at a spatial resolution in the range of meters. Together with high resolution AUV bathymetry this revealed a correlation of small-scale terrain variations (〈 5 m horizontally, 〈 1 m vertically) with nodule abundance. In the presented data set, increased nodule coverage could be correlated with slopes 〉 1.8° and concave terrain. On a more regional scale, factors such as the geological setting (existence of horst and graben structures, sediment thickness, outcropping basement) and influence of bottom currents seem to play an essential role for the spatial variation of nodule abundance and the related hard substrate habitat. AUV imagery was also successfully employed to map the distribution of re-settled sediment following a disturbance and sediment cloud generation during a sampling deployment of an Epibenthic Sledge. Data from before and after the "disturbance" allows a direct assessment of the impact. Automated image processing analyzed the nodule coverage at the seafloor, revealing nodule blanketing by resettling of suspended sediment within 16 hours after the disturbance. The visually detectable impact was spatially limited to a maximum of 100m distance from the disturbance track, downstream of the bottom water current. A correlation with high resolution AUV bathymetry reveals that the blanketing pattern varies in extent by tens of meters, strictly following the bathymetry, even in areas of only slightly undulating seafloor (〈 1 m vertical change). These results highlight the importance of detailed terrain knowledge when engaging in resource assessment studies for nodule abundance estimates and defining minable areas. At the same time, it shows the importance of high resolution mapping for detailed benthic habitat studies that show a heterogeneity at scales of 10 m to 100 m. Terrain knowledge is also needed to determine the scale of the impact by seafloor sediment blanketing during mining-operations.
    Type: Article , PeerReviewed , info:eu-repo/semantics/article
    Format: text
    DOI: 10.5194/bg-15-2525-2018
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    OceanRep: Article in a Scientific Journal - peer-reviewed
  • 3
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    TuLUMIS - a tunable LED-based underwater multispectral imaging system (2018)
    OSA (Optical Society of America)
    In:  Optics Express, 26 (6). pp. 7811-7828.
    Details
    Publication Date: 2021-03-19
    Description: Multispectral imaging (MSI) is widely used in terrestrial applications to help increase the discriminability between objects of interest. While MSI has shown potential for underwater geological and biological surveys, it is thus far rarely applied underwater. This is primarily due to the fact light propagation in water is subject to wavelength dependent attenuation and tough working conditions in the deep ocean. In this paper, a novel underwater MSI system based on a tunable light source is presented which employs a monochrome still image camera with flashing, pressure neutral color LEDs. Laboratory experiments and field tests were performed. Results from the lab experiments show an improvement of 76.66% on discriminating colors on a checkerboard by using the proposed imaging system over the use of an RGB camera. The field tests provided in situ MSI observations of pelagic fauna, and showed the first evidence that the system is capable of acquiring useful imagery under real marine conditions.
    Type: Article , PeerReviewed
    Format: text
    DOI: 10.1364/OE.26.007811
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    OceanRep: Article in a Scientific Journal - peer-reviewed
  • 4
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    Quantitative mapping and predictive modeling of Mn nodules' distribution from hydroacoustic and optical AUV data linked by random forests machine learning (2018)
    Copernicus Publications (EGU)
    In:  Biogeosciences (BG), 15 (23). pp. 7347-7377.
    Details
    Publication Date: 2021-03-19
    Description: In this study, high-resolution bathymetric multibeam and optical image data, both obtained within the Belgian manganese (Mn) nodule mining license area by the autonomous underwater vehicle (AUV) Abyss, were combined in order to create a predictive random forests (RF) machine learning model. AUV bathymetry reveals small-scale terrain variations, allowing slope estimations and calculation of bathymetric derivatives such as slope, curvature, and ruggedness. Optical AUV imagery provides quantitative information regarding the distribution (number and median size) of Mn nodules. Within the area considered in this study, Mn nodules show a heterogeneous and spatially clustered pattern, and their number per square meter is negatively correlated with their median size. A prediction of the number of Mn nodules was achieved by combining information derived from the acoustic and optical data using a RF model. This model was tuned by examining the influence of the training set size, the number of growing trees (ntree), and the number of predictor variables to be randomly selected at each node (mtry) on the RF prediction accuracy. The use of larger training data sets with higher ntree and mtry values increases the accuracy. To estimate the Mn-nodule abundance, these predictions were linked to ground-truth data acquired by box coring. Linking optical and hydroacoustic data revealed a nonlinear relationship between the Mn-nodule distribution and topographic characteristics. This highlights the importance of a detailed terrain reconstruction for a predictive modeling of Mn-nodule abundance. In addition, this study underlines the necessity of a sufficient spatial distribution of the optical data to provide reliable modeling input for the RF.
    Type: Article , PeerReviewed
    Format: text
    DOI: 10.5194/bg-15-7347-2018
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    OceanRep: Article in a Scientific Journal - peer-reviewed
  • 5
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    Compact-Morphology-based poly-metallic Nodule Delineation (2017)
    Nature Research
    In:  Scientific Reports, 7 (13338 ).
    Details
    Publication Date: 2020-06-18
    Description: 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.
    Type: Article , PeerReviewed
    Format: text
    DOI: 10.1038/s41598-017-13335-x
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    OceanRep: Article in a Scientific Journal - peer-reviewed
  • 6
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    An acquisition, curation and management workflow for sustainable, terabyte-scale marine image analysis (2018)
    Nature Research
    In:  Scientific Data, 5 (180181).
    Details
    Publication Date: 2021-03-19
    Description: Optical imaging is a common technique in ocean research. Diving robots, towed cameras, drop-cameras and TV-guided sampling gear: all produce image data of the underwater environment. Technological advances like 4K cameras, autonomous robots, high-capacity batteries and LED lighting now allow systematic optical monitoring at large spatial scale and shorter time but with increased data volume and velocity. Volume and velocity are further increased by growing fleets and emerging swarms of autonomous vehicles creating big data sets in parallel. This generates a need for automated data processing to harvest maximum information. Systematic data analysis benefits from calibrated, geo-referenced data with clear metadata description, particularly for machine vision and machine learning. Hence, the expensive data acquisition must be documented, data should be curated as soon as possible, backed up and made publicly available. Here, we present a workflow towards sustainable marine image analysis. We describe guidelines for data acquisition, curation and management and apply it to the use case of a multi-terabyte deep-sea data set acquired by an autonomous underwater vehicle.
    Type: Article , PeerReviewed
    Format: text
    DOI: 10.1038/sdata.2018.181
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    OceanRep: Article in a Scientific Journal - peer-reviewed
  • 7
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    Comparison of image annotation data generated by multiple investigators for benthic ecology (2016)
    Inter Research
    In:  Marine Ecology Progress Series, 552 . pp. 61-70.
    Details
    Publication Date: 2019-02-01
    Description: Multiple investigators often generate data from seabed images within a single image set to reduce the time burden, particularly with the large photographic surveys now available to ecological studies. These data (annotations) are known to vary as a result of differences in investigator opinion on specimen classification and of human factors such as fatigue and cognition. These variations are rarely recorded or quantified, nor are their impacts on derived ecological metrics (density, diversity, composition). We compared the annotations of 3 investigators of 73 megafaunal morphotypes in ~28 000 images, including 650 common images. Successful annotation was defined as both detecting and correctly classifying a specimen. Estimated specimen detection success was 77%, and classification success was 95%, giving an annotation success rate of 73%. Specimen detection success varied substantially by morphotype (12-100%). Variation in the detection of common taxa resulted in significant differences in apparent faunal density and community composition among investigators. Such bias has the potential to produce spurious ecological interpretations if not appropriately controlled or accounted for. We recommend that photographic studies document the use of multiple annotators and quantify potential inter-investigator bias. Randomisation of the sampling unit (photograph or video clip) is clearly critical to the effective removal of human annotation bias in multiple annotator studies (and indeed single annotator works).
    Type: Article , PeerReviewed
    Format: text
    Format: text
    DOI: 10.3354/meps11775
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    OceanRep: Article in a Scientific Journal - peer-reviewed
  • 8
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    Perspectives in visual imaging for marine biology and ecology: from acquisition to understanding (2016)
    CRC Press
    In:  Oceanography and Marine Biology: An Annual Review, 54 . pp. 1-72.
    Details
    Publication Date: 2019-09-23
    Type: Article , PeerReviewed
    Format: text
    DOI: 10.1201/9781315368597
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    OceanRep: Article in a Scientific Journal - peer-reviewed
  • 9
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    Current and future trends in marine image annotation software (2016)
    Elsevier
    In:  Progress in Oceanography, 149 . pp. 106-120.
    Details
    Publication Date: 2019-09-23
    Description: Highlights • Marine Image Annotation Software (MIAS) are used to assist annotation of underwater imagery. • We compare 23 MIAS assisting human annotation including some that include automated annotation. • MIAS can run in real time (50%), allow posterior annotation (95%), and interact with databases and data flows (44%). • MIAS differ in data input/output and display, customization, image analysis and re-annotation. • We provide important considerations when selecting UIAS, and outline future trends. Abstract Given the need to describe, analyze and index large quantities of marine imagery data for exploration and monitoring activities, a range of specialized image annotation tools have been developed worldwide. Image annotation - the process of transposing objects or events represented in a video or still image to the semantic level, may involve human interactions and computer-assisted solutions. Marine image annotation software (MIAS) have enabled over 500 publications to date. We review the functioning, application trends and developments, by comparing general and advanced features of 23 different tools utilized in underwater image analysis. MIAS requiring human input are basically a graphical user interface, with a video player or image browser that recognizes a specific time code or image code, allowing to log events in a time-stamped (and/or geo-referenced) manner. MIAS differ from similar software by the capability of integrating data associated to video collection, the most simple being the position coordinates of the video recording platform. MIAS have three main characteristics: annotating events in real time, in posteriorly to annotation and interact with a database. These range from simple annotation interfaces, to full onboard data management systems, with a variety of toolboxes. Advanced packages allow to input and display of data from multiple sensors or multiple annotators via intranet or internet. Posterior human-mediated annotation often include tools for data display and image analysis, e.g. length, area, image segmentation, point count; and in a few cases the possibility of browsing and editing previous dive logs or to analyze annotation data. The interaction with a database allows the automatic integration of annotations from different surveys, repeated annotation and collaborative annotation of shared datasets, browsing and querying of data. Progress in the field of automated annotation is mostly in post processing, for stable platforms or still images. Integration into available MIAS is currently limited to semi-automated processes of pixel recognition through computer-vision modules that compile expert-based knowledge. Important topics aiding the choice of a specific software are outlined, the ideal software is discussed and future trends are presented.
    Type: Article , PeerReviewed
    Format: text
    DOI: 10.1016/j.pocean.2016.07.005
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    OceanRep: Article in a Scientific Journal - peer-reviewed
  • 10
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    Fully automated image segmentation for benthic resource assessment of poly-metallic nodules (2016)
    Elsevier
    In:  Methods in Oceanography, 15-16 . pp. 78-89.
    Details
    Publication Date: 2020-06-26
    Description: Highlights • The proposed method automatically assesses the abundance of poly-metallic nodules on the seafloor. • No manually created feature reference set is required. • Large collections of benthic images from a range of acquisition gear can be analysed efficiently. Abstract Underwater image analysis is a new field for computational pattern recognition. In academia as well as in the industry, it is more and more common to use camera-equipped stationary landers, autonomous underwater vehicles, ocean floor observatory systems or remotely operated vehicles for image based monitoring and exploration. The resulting image collections create a bottleneck for manual data interpretation owing to their size. In this paper, the problem of measuring size and abundance of poly-metallic nodules in benthic images is considered. A foreground/background separation (i.e. separating the nodules from the surrounding sediment) is required to determine the targeted quantities. Poly-metallic nodules are compact (convex), but vary in size and appear as composites with different visual features (color, texture, etc.). Methods for automating nodule segmentation have so far relied on manual training data. However, a hand-drawn, ground-truthed segmentation of nodules and sediment is difficult (or even impossible) to achieve for a sufficient number of images. The new ES4C algorithm (Evolutionary tuned Segmentation using Cluster Co-occurrence and a Convexity Criterion) is presented that can be applied to a segmentation task without a reference ground truth. First, a learning vector quantization groups the visual features in the images into clusters. Secondly, a segmentation function is constructed by assigning the clusters to classes automatically according to defined heuristics. Using evolutionary algorithms, a quality criterion is maximized to assign cluster prototypes to classes. This criterion integrates the morphological compactness of the nodules as well as feature similarity in different parts of nodules. To assess its applicability, the ES4C algorithm is tested with two real-world data sets. For one of these data sets, a reference gold standard is available and we report a sensitivity of 0.88 and a specificity of 0.65. Our results show that the applied heuristics, which combine patterns in the feature domain with patterns in the spatial domain, lead to good segmentation results and allow full automation of the resource-abundance assessment for benthic poly-metallic nodules.
    Type: Article , PeerReviewed , info:eu-repo/semantics/article
    Format: text
    DOI: 10.1016/j.mio.2016.04.002
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    OceanRep: Article in a Scientific Journal - peer-reviewed
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