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Special Issue "Remote Sensing for Maritime Monitoring and Vessel Identification"

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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing Image Processing".

Deadline for manuscript submissions: 30 April 2023 | Viewed by 2021

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Special Issue Editors

Dr. Emanuele Salerno

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Guest Editor

Senior Researcher, Institute of Information Science and Technologies, National Research Council of Italy, 56124 Pisa, Italy
Interests: inverse problems; image processing; image analysis; microwave techniques

Dr. Claudio Di Paola

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Guest Editor

Head of Satellite Ground Segment, MapSat Srl, 82100 Benevento, Italy
Interests: ground segment services for polar orbiting satellites, maritime surveillance; earth observation data and image processing

Dr. Angelica Lo Duca

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Guest Editor

Institute of Informatics and Telematics – National Research Council (IIT-CNR), 56124 Pisa, Italy
Interests: data science; data narrative; web applications; machine learning; cultural heritage; tourism
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Special Issue Information

Dear Colleagues,

According to the statistics published by the United Nations Conference on Trade and Development, the total fleet worldwide consisted of more than 2,000,000,000 deadweight tons by 2021, against less than 700,000,000 in 1980. This increase in tonnage, and the total number of merchant ships, makes marine traffic surveillance essential for border control, monitoring of illegal activities as well as general security and emergency management. Wherever and whenever the collaborative vessel traffic services are not operational, or some vessel is suspected of sending falsified messages, remote sensing is the only possibility to properly ensure safety and security and take the appropriate reactions/countermeasures for any targeted event. Currently, this is still an open problem, even though many technologies and platforms are available for detecting and locating even the faintest objects on the sea surface, ranging from optics in various bands to radio/acoustic waves, and from satellite to underwater platforms. Besides detection and location, however, classification/identification and behavior analysis are also essential to deploy an effective monitoring system potentially insensitive to the collaborative status of the vessels transiting the surveilled area. The most advanced information technologies are needed to reach this goal, leveraging as much information as possible from as many useful sources as possible, including multi-platform sensors of any kind, and possible data from collaborative identification systems such as AIS, as well as relevant geographical and historical data. Apparently, pattern recognition, image analysis, statistical signal processing, classification, machine learning/deep learning and data science are the enabling technologies to equip detection and location results with the additional information that enables the surveillance authorities to be aware of any possible situation.

The aim of this Special Issue is to gather a number of papers from researchers active in this field, able to give the reader a comprehensive panorama of theory and practice or remote-sensing applications/systems dedicated to maritime surveillance.

Original submissions are welcome dealing with both theoretical and application aspects of the following list of topics.

Platforms
- Spaceborne
- Airborne
- Surface
- Underwater
Sensors
- Optical – panchromatic, multi/hyperspectral
- Thermal infrared
- Radar
- Acoustic
Data processing
- Detection
- Classification – identification
- Behavior analysis (speed, bearing, possible anomalies)
- Tracking
- Route prediction
- Data fusion with auxiliary data from collaborative systems
- Data fusion with geographical/historical data

Dr. Emanuele Salerno
Dr. Claudio Di Paola
Dr. Angelica Lo Duca
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 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. Remote Sensing 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 2500 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

maritime traffic monitoring
ship classification
remote sensing platforms/sensors
ship behavior analysis
data fusion
machine learning
artificial intelligence

Published Papers (2 papers)

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Research

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Open AccessArticle

Ship Classification in SAR Imagery by Shallow CNN Pre-Trained on Task-Specific Dataset with Feature Refinement

and

Remote Sens. 2022, 14(23), 5986; https://doi.org/10.3390/rs14235986 - 25 Nov 2022

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Abstract

Ship classification based on high-resolution synthetic aperture radar (SAR) imagery plays an increasingly important role in various maritime affairs, such as marine transportation management, maritime emergency rescue, marine pollution prevention and control, marine security situational awareness, and so on. The technology of deep learning, especially convolution neural network (CNN), has shown excellent performance on ship classification in SAR images. Nevertheless, it still has some limitations in real-world applications that need to be taken seriously by researchers. One is the insufficient number of SAR ship training samples, which limits the learning of satisfactory CNN, and the other is the limited information that SAR images can provide (compared with natural images), which limits the extraction of discriminative features. To alleviate the limitation caused by insufficient training datasets, one of the widely adopted strategies is to pre-train CNNs on a generic dataset with massive labeled samples (such as ImageNet) and fine-tune the pre-trained network on the target dataset (i.e., a SAR dataset) with a small number of training samples. However, recent studies have shown that due to the different imaging mechanisms between SAR and natural images, it is hard to guarantee that the pre-trained CNNs (even if they perform extremely well on ImageNet) can be finely tuned by a SAR dataset. On the other hand, to extract the most discriminative ship representation features from SAR images, the existing methods have carried out fruitful research on network architecture design, attention mechanism embedding, feature fusion, etc. Although these efforts improve the performance of SAR ship classification to some extent, they are usually based on more complex network architecture and higher dimensional features, accompanied by more time-consuming storage expenses. Through the analysis of SAR image characteristics and CNN feature extraction mechanism, this study puts forward three hypotheses: (1) Pre-training CNN on a task-specific dataset may be more effective than that on a generic dataset; (2) a shallow CNN may be more suitable for SAR image feature extraction than a deep one; and (3) the deep features extracted by CNNs can be further refined to improve the feature discrimination ability. To validate these hypotheses, we propose to learn a shallow CNN which is pre-trained on a task-specific dataset, i.e., the optical remote sensing ship dataset (ORS) instead of on the widely adopted ImageNet dataset. For comparison purposes, we designed 28 CNN architectures by changing the arrangement of the CNN components, the size of convolutional filters, and pooling formulations based on VGGNet models. To further reduce redundancy and improve the discrimination ability of the deep features, we propose to refine deep features by active convolutional filter selection based on the coefficient of variation (COV) sorting criteria. Extensive experiments not only prove that the above hypotheses are valid but also prove that the shallow network learned by the proposed pre-training strategy and the feature refining method can achieve considerable ship classification performance in SAR images like the state-of-the-art (SOTA) methods. Full article

(This article belongs to the Special Issue Remote Sensing for Maritime Monitoring and Vessel Identification)

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Review

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Open AccessReview

Evolution of Maritime GNSS and RNSS Performance Standards

Paweł Zalewski

Andrzej Bąk

and

Michael Bergmann

Remote Sens. 2022, 14(21), 5291; https://doi.org/10.3390/rs14215291 - 22 Oct 2022

Viewed by 714

Abstract

The primary means for electronic position fixing in use in contemporary maritime transport are shipborne GPS (Global Positioning System) receivers or DGPS (Differential GPS) receivers. More advanced GNSS (Global Navigation Satellite System) or RNSS (Regional Navigation Satellite Systems) receivers are able to process combined signals from American GPS, Russian GLONASS, Chinese Beidou (BDS), European Galileo, Indian IRNSS, and Japan QZSS. Satellite-based augmentation systems (SBAS) are still not commonly used in the maritime domain, especially onboard vessels certified under international SOLAS convention. The issues and weaknesses of existing International Maritime Organization recommendations, guidelines, requirements, performance standards, and policies on GNSS shipborne sensors are discussed and presented in the paper. Many problems that have already been dealt with in other means of transportation are still to be solved in the maritime domain. The integrity monitoring is addressed as the main issue, and recommendations based on solutions implemented in aviation and the latest research are proposed. Finally, the strengths, weaknesses, opportunities, and threats awaiting maritime GNSS standardization process are outlined. Full article

(This article belongs to the Special Issue Remote Sensing for Maritime Monitoring and Vessel Identification)

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Planned Papers

The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.

Title: Data Fusion for Vessel Tracking
Authors: Dimitris Zissis
Affiliation: Department of Product and System Engineering, University of the Aegean, A1.1, Konstantinoupoleos 2, Syros, Greece

Title: Evolution of Maritime GNSS Performance Standards
Authors: Paweł Zalewski
Affiliation: Department of Maritime Simulation, Faculty of Navigation, Maritime University of Szczecin, Wały Chrobrego St. 1-2, 70-500 Szczecin, Poland

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