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Remote Sensing in Vessel Detection and Navigation: Edition Ⅱ
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Remote Sensing in Vessel Detection and Navigation: Edition Ⅱ

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

Deadline for manuscript submissions: closed (15 February 2022) | Viewed by 7800

Special Issue Editor

Prof. Dr. Andrzej Stateczny

E-Mail Website
Guest Editor
Department of Geodesy, Faculty of Civil and Environmental Engineering, Gdansk Technical University, Narutowicza St. 11/12, Gdansk, Poland
Interests: radar navigation; comparative (terrain-based) navigation; multi-sensor data fusion; radar and sonar target tracking; sonar imaging and understanding; MBES bathymetry; ASV; artificial neural networks; geoinformatics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Earth observation by multispectral, SAR, and other sensors provides unique global as well as detailed local surveillance. Resolutions allow for vessel detection, classification, and discrimination from, e.g., icebergs and other objects. Important applications include vessel detection and navigation; trafficking and safety; and monitoring the oceans for fishing, oil slicks, territorial violations, piracy, refugee boats, etc. With global warming, the northeast and -west passages have opened up for shipping, fishing, and cruise ships in uncharted reef-infested territories littered with sea ice and titanic icebergs.

In this Special Issue of Sensors, we will collect articles covering many aspects of multispectral, multisensor, SAR, and other sensors related to science/research, algorithm/technical development, analysis tools, synergy with sensors in multiple wavelengths of the EM spectrum, synergy with other measurements such as AIS, as well as reviews of the state of the art in ocean processes using multispectral and SAR imagery for oceans and sea ice, and vessel monitoring for surveillance, trafficking, and navigation. Topics for this Special Issue include but are not limited to the following:

  • Vessel detection, classification, and identification;
  • Sea ice and iceberg detection and tracking;
  • Multisensor data fusion;
  • Autonomous ships navigation;
  • Comparative (terrain reference) navigation;
  • Change detection for classifying islands, reefs, and other static objects;
  • Synergy with and comparison to AIS and other vessel identification data;
  • Synergies between satellite sensors with airborne platforms; multiple satellite SAR; optical and thermal infrared sensors including finer resolution sensors, for example, sentinels and other satellites, and in situ measurements;
  • The use of multispectral, multiple frequencies, and polarizations to interpret and quantitatively assess various ocean surfaces, currents, and sea-ice phenomena for navigation;
  • Interferometric and Doppler-derived SAR oceanic and sea-ice applications focused on surface motion;
  • Validation studies for vessel, ocean, and sea-ice parameters based on in situ and airborne data collections;
  • Use of machine learning and the build-up of annotated training databases;
  • Artificial Intelligence for image data processing.

Prof. Dr. Andrzej Stateczny
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 submissions that pass pre-check are 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 2600 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 (2 papers)

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Research

21 pages, 17934 KiB  
Article
Probabilistic Maritime Trajectory Prediction in Complex Scenarios Using Deep Learning
by Kristian Aalling Sørensen, Peder Heiselberg and Henning Heiselberg
Sensors 2022, 22(5), 2058; https://doi.org/10.3390/s22052058 - 07 Mar 2022
Cited by 25 | Viewed by 3495
Abstract
Maritime activity is expected to increase, and therefore also the need for maritime surveillance and safety. Most ships are obligated to identify themselves with a transponder system like the Automatic Identification System (AIS) and ships that do not, intentionally or unintentionally, are referred [...] Read more.
Maritime activity is expected to increase, and therefore also the need for maritime surveillance and safety. Most ships are obligated to identify themselves with a transponder system like the Automatic Identification System (AIS) and ships that do not, intentionally or unintentionally, are referred to as dark ships and must be observed by other means. Knowing the future location of ships can not only help with ship/ship collision avoidance, but also with determining the identity of these dark ships found in, e.g., satellite images. However, predicting the future location of ships is inherently probabilistic and the variety of possible routes is almost limitless. We therefore introduce a Bidirectional Long-Short-Term-Memory Mixture Density Network (BLSTM-MDN) deep learning model capable of characterising the underlying distribution of ship trajectories. It is consequently possible to predict a probabilistic future location as opposed to a deterministic location. AIS data from 3631 different cargo ships are acquired from a region west of Norway spanning 320,000 sqkm. Our implemented BLSTM-MDN model characterizes the conditional probability of the target, conditioned on an input trajectory using an 11-dimensional Gaussian distribution and by inferring a single target from the distribution, we can predict several probable trajectories from the same input trajectory with a test Negative Log Likelihood loss of −9.96 corresponding to a mean distance error of 2.53 km 50 min into the future. We compare our model to both a standard BLSTM and a state-of-the-art multi-headed self-attention BLSTM model and the BLSTM-MDN performs similarly to the two deterministic deep learning models on straight trajectories, but produced better results in complex scenarios. Full article
(This article belongs to the Special Issue Remote Sensing in Vessel Detection and Navigation: Edition Ⅱ)
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30 pages, 20321 KiB  
Article
Wireless Local Area Network Technologies as Communication Solutions for Unmanned Surface Vehicles
by Andrzej Stateczny, Krzysztof Gierlowski and Michal Hoeft
Sensors 2022, 22(2), 655; https://doi.org/10.3390/s22020655 - 15 Jan 2022
Cited by 9 | Viewed by 3391
Abstract
As the number of research activities and practical deployments of unmanned vehicles has shown a rapid growth, topics related to their communication with operator and external infrastructure became of high importance. As a result a trend of employing IP communication for this purpose [...] Read more.
As the number of research activities and practical deployments of unmanned vehicles has shown a rapid growth, topics related to their communication with operator and external infrastructure became of high importance. As a result a trend of employing IP communication for this purpose is emerging and can be expected to bring significant advantages. However, its employment can be expected to be most effective using broadband communication technologies such as Wireless Local Area Networks (WLANs). To verify the effectiveness of such an approach in a specific case of surface unmanned vehicles, the paper includes an overview of IP-based MAVLink communication advantages and requirements, followed by a laboratory and field-experiment study of selected WLAN technologies, compared to popular narrowband communication solutions. The conclusions confirm the general applicability of IP/WLAN communication for surface unmanned vehicles, providing an overview of their advantages and pointing out deployment requirements. Full article
(This article belongs to the Special Issue Remote Sensing in Vessel Detection and Navigation: Edition Ⅱ)
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