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Special Issue "Sensor Applications on Marine Recognition"

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Remote Sensors".

Deadline for manuscript submissions: 30 September 2020.

Special Issue Editor

Prof. Dr. Bogusław Cyganek
Website
Guest Editor
Faculty of Computer Science, Electronics and Telecommunication, Department of Electronics, AGH University of Science and Technology, Al. Mickiewicza 30/C2/413, 30-059 Krakow, Poland
Interests: computer vision; AI; deep learning; pattern recognition; underwater technologies; embedded systems

Special Issue Information

Dear Colleagues,

The ocean, covering over 70% of our planet and reaching depths of nearly 11,000 meters, is the last largely unexplored environment on Earth. Since a vast majority of electromagnetic waves, including light, penetrate the ocean depths only in a very limited degree, sensing and exploring the immense ocean environment presents exceptional challenges. Despite great advances in the sensing technologies, there are still serious challenges in their successful applications in various domains of marine recognition. Hence, development of novel methods, joining modern sensing techniques operating with diverse signal types, is of primary interest for the research and marine industry.

The purpose of this Special Issue is to provide a platform for information exchange and knowledge sharing, as well as to gather the latest research achievements, in the broad subject of sensor applications on marine recognition viewed from both the science and industry corners.

Original submissions from all areas related to sensor applications on marine recognition are welcome. Topics of interest include but are not limited to the following ones.

Keywords:

  • Marine sensors technologies
  • Marine recognition for biology
  • Underwater search and exploration
  • Sensing for autonomous underwater drones
  • Underwater drones navigation
  • Deep learning and AI methods in marine recognition
  • Deep-sea multispectral and hyperspectral sensing
  • Computer vision and image processing methods for marine recognition
  • Automated detection, classification, and segmentation of marine objects
  • Underwater image and video restoration methods
  • Tensor based methods for multidimensional signal processing
  • Deep-sea optical imaging technologies
  • Acoustic imaging in marine recognition
  • Recognition from sidescan and multibeam sonars
  • Restoration and filtering of the sidescan and multibeam sonar signals
  • Seismic monitoring and imaging
  • Multichannel seismic technologies
  • Data management, annotations, access, visualization, and sharing
Prof. Dr. Bogusław Cyganek

Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2000 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (4 papers)

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Research

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Open AccessArticle
MorphoCluster: Efficient Annotation of Plankton Images by Clustering
Sensors 2020, 20(11), 3060; https://doi.org/10.3390/s20113060 (registering DOI) - 28 May 2020
Abstract
In this work, we present MorphoCluster, a software tool for data-driven, fast, and accurate annotation of large image data sets. While already having surpassed the annotation rate of human experts, volume and complexity of marine data will continue to increase in the coming [...] Read more.
In this work, we present MorphoCluster, a software tool for data-driven, fast, and accurate annotation of large image data sets. While already having surpassed the annotation rate of human experts, volume and complexity of marine data will continue to increase in the coming years. Still, this data requires interpretation. MorphoCluster augments the human ability to discover patterns and perform object classification in large amounts of data by embedding unsupervised clustering in an interactive process. By aggregating similar images into clusters, our novel approach to image annotation increases consistency, multiplies the throughput of an annotator, and allows experts to adapt the granularity of their sorting scheme to the structure in the data. By sorting a set of 1.2 M objects into 280 data-driven classes in 71 h (16 k objects per hour), with 90% of these classes having a precision of 0.889 or higher. This shows that MorphoCluster is at the same time fast, accurate, and consistent; provides a fine-grained and data-driven classification; and enables novelty detection. Full article
(This article belongs to the Special Issue Sensor Applications on Marine Recognition)
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Open AccessArticle
Combining Denoising Autoencoders and Dynamic Programming for Acoustic Detection and Tracking of Underwater Moving Targets
Sensors 2020, 20(10), 2945; https://doi.org/10.3390/s20102945 - 22 May 2020
Abstract
Accurate detection and tracking of moving targets in underwater environments pose significant challenges, because noise in acoustic measurements (e.g., SONAR) makes the signal highly stochastic. In continuous marine monitoring a further challenge is related to the computational complexity of the signal processing pipeline—due [...] Read more.
Accurate detection and tracking of moving targets in underwater environments pose significant challenges, because noise in acoustic measurements (e.g., SONAR) makes the signal highly stochastic. In continuous marine monitoring a further challenge is related to the computational complexity of the signal processing pipeline—due to energy constraints, in off-shore monitoring platforms algorithms should operate in real time with limited power consumption. In this paper, we present an innovative method that allows to accurately detect and track underwater moving targets from the reflections of an active acoustic emitter. Our system is based on a computationally- and energy-efficient pre-processing stage carried out using a deep convolutional denoising autoencoder (CDA), whose output is then fed to a probabilistic tracking method based on the Viterbi algorithm. The CDA is trained on a large database of more than 20,000 reflection patterns collected during 50 designated sea experiments. System performance is then evaluated on a controlled dataset, for which ground truth information is known, as well as on recordings collected during different sea experiments. Results show that, compared to the benchmark, our method achieves a favorable trade-off between detection and false alarm rate, as well as improved tracking accuracy. Full article
(This article belongs to the Special Issue Sensor Applications on Marine Recognition)
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Open AccessArticle
Ship Target Automatic Detection Based on Hypercomplex Flourier Transform Saliency Model in High Spatial Resolution Remote-Sensing Images
Sensors 2020, 20(9), 2536; https://doi.org/10.3390/s20092536 - 29 Apr 2020
Abstract
Efficient ship detection is essential to the strategies of commerce and military. However, traditional ship detection methods have low detection efficiency and poor reliability due to uncertain conditions of the sea surface, such as the atmosphere, illumination, clouds and islands. Hence, in this [...] Read more.
Efficient ship detection is essential to the strategies of commerce and military. However, traditional ship detection methods have low detection efficiency and poor reliability due to uncertain conditions of the sea surface, such as the atmosphere, illumination, clouds and islands. Hence, in this study, a novel ship target automatic detection system based on a modified hypercomplex Flourier transform (MHFT) saliency model is proposed for spatial resolution of remote-sensing images. The method first utilizes visual saliency theory to effectively suppress sea surface interference. Then we use OTSU methods to extract regions of interest. After obtaining the candidate ship target regions, we get the candidate target using a method of ship target recognition based on ResNet framework. This method has better accuracy and better performance for the recognition of ship targets than other methods. The experimental results show that the proposed method not only accurately and effectively recognizes ship targets, but also is suitable for spatial resolution of remote-sensing images with complex backgrounds. Full article
(This article belongs to the Special Issue Sensor Applications on Marine Recognition)
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Review

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Open AccessReview
Sensors to Increase the Security of Underwater Communication Cables: A Review of Underwater Monitoring Sensors
Sensors 2020, 20(3), 737; https://doi.org/10.3390/s20030737 - 29 Jan 2020
Cited by 1
Abstract
Underwater communication cables transport large amounts of sensitive information between countries. This fact converts these cables into a critical infrastructure that must be protected. Monitoring the underwater cable environment is rare and any intervention is usually driven by cable faults. In the last [...] Read more.
Underwater communication cables transport large amounts of sensitive information between countries. This fact converts these cables into a critical infrastructure that must be protected. Monitoring the underwater cable environment is rare and any intervention is usually driven by cable faults. In the last few years, several reports raised issues about possible future malicious attacks on such cables. The main objective of this operational research and analysis (ORA) paper is to present an overview of different commercial and already available marine sensor technologies (acoustic, optic, magnetic and oceanographic) that could be used for autonomous monitoring of the underwater cable environment. These sensors could be mounted on different autonomous platforms, such as unmanned surface vehicles (USVs) or autonomous underwater vehicles (AUVs). This paper analyses a multi-threat sabotage scenario where surveying a transatlantic cable of 13,000 km, (reaching water depths up to 4000 m) is necessary. The potential underwater threats identified for such a scenario are: divers, anchors, fishing trawls, submarines, remotely operated vehicles (ROVs) and AUVs. The paper discusses the capabilities of the identified sensors to detect such identified threats for the scenario under study. It also presents ideas on the construction of periodic and permanent surveillance networks. Research study and results are focused on providing useful information to decision-makers in charge of designing surveillance capabilities to secure underwater communication cables. Full article
(This article belongs to the Special Issue Sensor Applications on Marine Recognition)
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