Special Issue "Machine Learning and Remote Sensing in Ocean Science and Engineering"

A special issue of Journal of Marine Science and Engineering (ISSN 2077-1312). This special issue belongs to the section "Ocean Engineering".

Deadline for manuscript submissions: 10 April 2021.

Special Issue Editors

Assoc. Prof. Dr. Marco Cococcioni
Website
Guest Editor
Department of Information Engineering, School of Engineering, University of Pisa, Largo Lucio Lazzarino, 1-56123 Pisa, Italy
Interests: machine learning; computational intelligence; remote sensing; operations research; decision support systems
Assoc. Prof. Dr. Pierre Lermusiaux
Website
Guest Editor
Department of Mechanical Engineering, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, Massachusetts 02139, USA
Interests: ocean science and engineering; numerical ocean modeling; data assimilation; uncertainty quantification and inference schemes

Special Issue Information

Dear Colleagues,

The impressive recent advancements in machine learning and deep neural networks open new possibilities in ocean science and engineering. The use of convolutional neural networks to process remotely sensed multi- and hyper-spectral optical images is providing unprecedented classification opportunities in ship classification and tracking. The use of convolutional neural networks for remotely sensed SAR/ISAR images or side-scan sonar images is providing unprecedented classification accuracies. In addition, the use of autonomous platforms (such as underwater gliders, drifters, and floats) is providing a massive amount of ocean state measurements, which can be exploited by novel data assimilation schemes. The role of machine learning in ocean modelling and mining is growing at a constant pace. Autonomous surveillance and search and rescue operations are also benefiting from the availability of both satellite data and in situ data collected by AUVs. Computational intelligent techniques have a clear potential to help in solving these complex tasks, frequently characterized by multiple conflicting objectives.

The purpose of the invited Special Issue is to publish the most exciting research with respect to the above subjects and to provide a rapid turn-around time regarding reviewing and publishing, and to disseminate the articles freely for research, teaching, and reference purposes.

We encourage the submission of high-quality papers which are directly related to various aspects, as mentioned below. Novel techniques are encouraged.


Assoc. Prof. Dr. Marco Cococcioni
Assoc. Prof. Dr. Pierre Lermusiaux
Guest Editors

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. Journal of Marine Science and Engineering is an international peer-reviewed open access monthly 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 1400 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.

Keywords

  • machine learning/deep learning/artificial intelligence
  • data fusion and data mining
  • remote sensing of the ocean
  • port and ship protection
  • ROVs, AUVs, USVs, underwater gliders
  • maritime big data analysis and mining
  • path planning and waypoint optimization
  • search and rescue operations
  • adaptive sampling/optimal sampling of the ocean
  • maritime situational awareness
  • ocean data assimilation
  • numerical ocean modeling
  • multi-objective optimization (evolutionary opt., swarm opt.)
  • counter piracy
  • autonomous surveillance
  • oil spill detection and tracking
  • optimization and control of autonomous ocean systems
  • remote bathymetry estimation
  • security and defense applications
  • ship classification from remotely sensed optical images
  • decision support systems

Published Papers (1 paper)

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Research

Open AccessArticle
A Study on Enhancement of Fish Recognition Using Cumulative Mean of YOLO Network in Underwater Video Images
J. Mar. Sci. Eng. 2020, 8(11), 952; https://doi.org/10.3390/jmse8110952 - 22 Nov 2020
Abstract
In the underwater environment, in order to preserve rare and endangered objects or to eliminate the exotic invasive species that can destroy the ecosystems, it is essential to classify objects and estimate their number. It is very difficult to classify objects and estimate [...] Read more.
In the underwater environment, in order to preserve rare and endangered objects or to eliminate the exotic invasive species that can destroy the ecosystems, it is essential to classify objects and estimate their number. It is very difficult to classify objects and estimate their number. While YOLO shows excellent performance in object recognition, it recognizes objects by processing the images of each frame independently of each other. By accumulating the object classification results from the past frames to the current frame, we propose a method to accurately classify objects, and count their number in sequential video images. This has a high classification probability of 93.94% and 97.06% in the test videos of Bluegill and Largemouth bass, respectively. The proposed method shows very good classification performance in video images taken of the underwater environment. Full article
(This article belongs to the Special Issue Machine Learning and Remote Sensing in Ocean Science and Engineering)
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