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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: closed (31 October 2023) | Viewed by 37954

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Special Issue Editors

Dr. Emanuele Salerno

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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 × 10⁹ deadweight tons by 2021, against less than 7 × 10⁸ 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 2700 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

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Published Papers (12 papers)

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Editorial

Jump to: Research, Review

4 pages, 404 KiB

Open AccessEditorial

Remote Sensing for Maritime Monitoring and Vessel Identification

by Emanuele Salerno, Claudio Di Paola and Angelica Lo Duca

Remote Sens. 2024, 16(5), 776; https://doi.org/10.3390/rs16050776 - 23 Feb 2024

Cited by 2 | Viewed by 2559

Abstract

According to the statistics published by the United Nations Conference on Trade and Development [1], the total fleet worldwide consisted of more than

2 [...]

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

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Research

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25 pages, 8453 KiB

Open AccessArticle

Remote Sensing for Maritime Traffic Understanding

by Marco Reggiannini, Emanuele Salerno, Clara Bacciu, Andrea D’Errico, Angelica Lo Duca, Andrea Marchetti, Massimo Martinelli, Costanzo Mercurio, Antonino Mistretta, Marco Righi, Marco Tampucci and Claudio Di Paola

Remote Sens. 2024, 16(3), 557; https://doi.org/10.3390/rs16030557 - 31 Jan 2024

Cited by 10 | Viewed by 3087

Abstract

The capability of prompt response in the case of critical circumstances occurring within a maritime scenario depends on the awareness level of the competent authorities. From this perspective, a quick and integrated surveillance service represents a tool of utmost importance. This is even more true when the main purpose is to tackle illegal activities such as smuggling, waste flooding, or malicious vessel trafficking. This work presents an improved version of the OSIRIS system, a previously developed Information and Communication Technology framework devoted to understanding the maritime vessel traffic through the exploitation of optical and radar data captured by satellite imaging sensors. A number of dedicated processing units are cascaded with the objective of (i) detecting the presence of vessel targets in the input imagery, (ii) estimating the vessel types on the basis of their geometric and scatterometric features, (iii) estimating the vessel kinematics, (iv) classifying the navigation behavior of the vessel and predicting its route, and, eventually, (v) integrating the several outcomes within a webGIS interface to easily assess the traffic status inside the considered area. The entire processing pipeline has been tested on satellite imagery captured within the Mediterranean Sea or extracted from public annotated datasets. Full article

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

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29 pages, 40869 KiB

Open AccessArticle

The Big Picture: An Improved Method for Mapping Shipping Activities

by Alexandros Troupiotis-Kapeliaris, Dimitris Zissis, Konstantina Bereta, Marios Vodas, Giannis Spiliopoulos and Giannis Karantaidis

Remote Sens. 2023, 15(21), 5080; https://doi.org/10.3390/rs15215080 - 24 Oct 2023

Cited by 2 | Viewed by 1972

Abstract

Density maps support a bird’s eye view of vessel traffic, through providing an overview of vessel behavior, either at a regional or global scale in a given timeframe. However, any inaccuracies in the underlying data, due to sensor noise or other factors, evidently lead to erroneous interpretations and misleading visualizations. In this work, we propose a novel algorithmic framework for generating highly accurate density maps of shipping activities, from incomplete data collected by the Automatic Identification System (AIS). The complete framework involves a number of computational steps for (1) cleaning and filtering AIS data, (2) improving the quality of the input dataset (through trajectory reconstruction and satellite image analysis) and (3) computing and visualizing the subsequent vessel traffic as density maps. The framework describes an end-to-end implementation pipeline for a real world system, capable of addressing several of the underlying issues of AIS datasets. Real-world data are used to demonstrate the effectiveness of our framework. These experiments show that our trajectory reconstruction method results in significant improvements up to 15% and 26% for temporal gaps of 3–6 and 6–24 h, respectively, in comparison to the baseline methodology. Additionally, a use case in European waters highlights our capability of detecting “dark vessels”, i.e., vessel positions not present in the AIS data. Full article

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

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21 pages, 10510 KiB

Open AccessArticle

Optical Remote Sensing Ship Recognition and Classification Based on Improved YOLOv5

by Jun Jian, Long Liu, Yingxiang Zhang, Ke Xu and Jiaxuan Yang

Remote Sens. 2023, 15(17), 4319; https://doi.org/10.3390/rs15174319 - 1 Sep 2023

Cited by 11 | Viewed by 2539

Abstract

Due to the special characteristics of the shooting distance and angle of remote sensing satellites, the pixel area of ship targets is small, and the feature expression is insufficient, which leads to unsatisfactory ship detection performance and even situations such as missed and false detection. To solve these problems, this paper proposes an improved-YOLOv5 algorithm mainly including: (1) Add the Convolutional Block Attention Module (CBAM) into the Backbone to enhance the extraction of target-adaptive optimal features; (2) Introduce a cross-layer connection channel and lightweight GSConv structures into the Neck to achieve higher-level multi-scale feature fusion and reduce the number of model parameters; (3) Use the Wise-IoU loss function to calculate the localization loss in the Output, and assign reasonable gradient gains to cope with differences in image quality. In addition, during the preprocessing stage of experimental data, a median+bilateral filter method was used to reduce interference from ripples and waves and highlight the information of ship features. The experimental results show that Improved-YOLOv5 has a significant improvement in recognition accuracy compared to various mainstream target detection algorithms; compared to the original YOLOv5s, the mean Average Precision (mAP) improved by 3.2% and the Frames Per Second (FPN) accelerated by 8.7%. Full article

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

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18 pages, 4631 KiB

Open AccessArticle

Marine Environmental Impact on CFAR Ship Detection as Measured by Wave Age in SAR Images

by Diego X. Bezerra, João A. Lorenzzetti and Rafael L. Paes

Remote Sens. 2023, 15(13), 3441; https://doi.org/10.3390/rs15133441 - 7 Jul 2023

Cited by 10 | Viewed by 1943

Abstract

Satellite synthetic aperture radar (SAR) images are recognized as one of the most efficient tools for day/night, all weather and large area monitoring of ships at sea. However, false alarms discrimination is still one key problem on SAR ship detection. While many discrimination techniques have been proposed for the treatment of false alarms, not enough emphasis has been targeted to explore how obtained false alarms are related to the changing ocean environmental conditions. To this end, we combined a large set of Sentinel-1 SAR images with ocean surface wind and wave data into one dataset. SAR images were separated into three distinct groups according to wave age (WA) conditions present during image acquisition: young wind sea, old wind sea, and swell. A constant false alarm rate (CFAR) ship detection algorithm was implemented based on the generalized gamma distribution (GΓD). Kolmogorov–Smirnov distance was used to analyze the distribution goodness-of-fit among distinct ocean environments. A backscattering analysis of different sizes of ship targets and sea clutter was further performed using the OpenSARShip and automatic identification system (AIS) datasets to assess its separability. We derived a discrimination threshold adjustment based on WA conditions and showed its efficacy to drastically reduce false alarms. To our present knowledge, the use of WA as part of the CFAR and for the adjustment of the threshold of detection is a novelty which could be tested and evaluated for different SAR sensors. Full article

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

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24 pages, 14117 KiB

Open AccessArticle

Building a Practical Multi-Sensor Platform for Monitoring Vessel Activity near Marine Protected Areas: Case Studies from Urban and Remote Locations

by Samantha Cope, Brendan Tougher, Virgil Zetterlind, Lisa Gilfillan and Andres Aldana

Remote Sens. 2023, 15(13), 3216; https://doi.org/10.3390/rs15133216 - 21 Jun 2023

Cited by 4 | Viewed by 4990

Abstract

Monitoring vessel activity is an important part of managing marine protected areas (MPAs), but small-scale fishing and recreational vessels that do not participate in cooperative vessel traffic systems require additional monitoring strategies. Marine Monitor (M2) is a shore-based, multi-sensor platform that integrates commercially available hardware, primarily X-band marine radar and optical cameras, with custom software to autonomously track and report on vessel activity regardless of participation in other tracking systems. By utilizing established commercial hardware, the radar system is appropriate for supporting the management of coastal, small-scale MPAs. Data collected in the field are transferred to the cloud to provide a continuous record of activity and identify prohibited activities in real-time using behavior characteristics. To support the needs of MPA managers, both hardware and software improvements have been made over time, including ruggedizing equipment for the marine environment and powering systems in remote locations. Case studies are presented comparing data collection by both radar and the Automatic Identification System (AIS) in urban and remote locations. At the South La Jolla State Marine Reserve near San Diego, CA, USA, 93% of vessel activity (defined as the cumulative time vessels spent in the MPA) was identified exclusively by radar from November 2022 through January 2023. At the Caye Bokel Conservation Area, within the Turneffe Atoll Marine Reserve offshore of Belize, 98% was identified exclusively by radar from April through October 2022. Spatial and temporal patterns of radar-detected and AIS activity also differed at both sites. These case study site results together demonstrate the common and persistent presence of small-scale vessel activity near coastal MPAs that is not documented by cooperative systems. Therefore, an integrated radar system can be a useful tool for independent monitoring, supporting a comprehensive understanding of vessel activity in a variety of areas. Full article

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

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16 pages, 9653 KiB

Open AccessCommunication

A Novel Deep Learning Network with Deformable Convolution and Attention Mechanisms for Complex Scenes Ship Detection in SAR Images

by Peng Chen, Hui Zhou, Ying Li, Peng Liu and Bingxin Liu

Remote Sens. 2023, 15(10), 2589; https://doi.org/10.3390/rs15102589 - 16 May 2023

Cited by 11 | Viewed by 2885

Abstract

Synthetic aperture radar (SAR) can detect objects in various climate and weather conditions. Therefore, SAR images are widely used for maritime object detection in applications such as maritime transportation safety and fishery law enforcement. However, nearshore ship targets in SAR images are often affected by background clutter, resulting in a low detection rate, high false alarm rate, and high missed detection rate, especially for small-scale ship targets. To address this problem, in this paper, we propose a novel deep learning network with deformable convolution and attention mechanisms to improve the Feature Pyramid Network (FPN) model for nearshore ship target detection in SAR images with complex backgrounds. The proposed model uses a deformable convolutional neural network in the feature extraction network to adapt the convolution position to the target sampling point, enhancing the feature extraction ability of the target, and improving the detection rate of the ship target against the complex background. Moreover, this model uses a channel attention mechanism to capture the feature dependencies between different channel graphs in the feature extraction network and reduce the false detection rate. The designed experiments on a public SAR image ship dataset show that our model achieves 87.9% detection accuracy for complex scenes and 95.1% detection accuracy for small-scale ship targets. A quantitative comparison of the proposed model with several classical and recently developed deep learning models on the same SAR images dataset demonstrated the superior performance of the proposed method over other models. Full article

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

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17 pages, 3124 KiB

Open AccessArticle

Maritime Infrared Target Detection Using a Dual-Mode Background Model

by Anran Zhou, Weixin Xie and Jihong Pei

Remote Sens. 2023, 15(9), 2354; https://doi.org/10.3390/rs15092354 - 29 Apr 2023

Cited by 4 | Viewed by 1770

Abstract

With the rapid development of marine business, the intelligent detection of ship targets has become the key to marine safety. However, it is difficult to accurately detect maritime infrared targets due to severe sea clutter interference in strong wind waves or dim sea scenes. To adapt to diverse marine environments, a dual-mode sea background model is proposed for target detection. According to the global contrast of the image, the scene is divided into the sea surface with violent changes and the sea surface with stable changes. In the first stage, the preliminary background model suitable for steadily changing scenes is proposed. The pixel-level foreground mask is generated through the background block filter and the posterior probability criterion. Moreover, the learning rate parameter is adjusted using the detection results of two adjacent frames. In the second stage, the background model suitable for highly fluctuating scenes is proposed. Moreover, the local correlation feature is used to enhance the local contrast of the frame. The experimental results for the different scenes show that the proposed method has a better detection performance than the other comparison algorithms. Full article

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

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21 pages, 10192 KiB

Open AccessArticle

Visual Detection and Association Tracking of Dim Small Ship Targets from Optical Image Sequences of Geostationary Satellite Using Multispectral Radiation Characteristics

by Fan Meng, Guocan Zhao, Guojun Zhang, Zhi Li and Kaimeng Ding

Remote Sens. 2023, 15(8), 2069; https://doi.org/10.3390/rs15082069 - 14 Apr 2023

Cited by 4 | Viewed by 2489

Abstract

By virtue of the merits of wide swath, persistent observation, and rapid operational response, geostationary remote sensing satellites (e.g., GF-4) show tremendous potential for sea target system surveillance and situational awareness. However, ships in such images appear as dim small targets and may be affected by clutter, reef islands, clouds, and other interferences, which makes the task of ship detection and tracking intractable. Considering the differences in visual saliency characteristics across multispectral bands between ships and jamming targets, a novel approach to visual detecting and association tracking of dense ships based on the GF-4 image sequences is proposed in this paper. First, candidate ship blobs are segmented in each single-spectral image of each frame through a multi-vision salient features fusion strategy, to obtain the centroid position, size, and corresponding spectral grayscale information of suspected ships. Due to the displacement of moving ships across multispectral images of each frame, multispectral association with regard to the positions of ship blobs is then performed to determine the final ship detections. Afterwards, precise position correction of detected ships is implemented for each frame in image sequences via multimodal data association between GF-4 detections and automatic identification system data. Last, an improved multiple hypotheses tracking algorithm with multispectral radiation and size characteristics is put forward to track ships across multi-frame corrected detections and estimate ships’ motion states. Experiment results demonstrate that our method can effectively detect and track ships in GF-4 remote sensing image sequences with high precision and recall rate, yielding state-of-the-art performance. Full article

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

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25 pages, 8045 KiB

Open AccessArticle

A Fine-Grained Ship-Radiated Noise Recognition System Using Deep Hybrid Neural Networks with Multi-Scale Features

by Shuai Liu, Xiaomei Fu, Hong Xu, Jiali Zhang, Anmin Zhang, Qingji Zhou and Hao Zhang

Remote Sens. 2023, 15(8), 2068; https://doi.org/10.3390/rs15082068 - 14 Apr 2023

Cited by 11 | Viewed by 2973

Abstract

Fine-grained ship-radiated noise recognition methods of different specific ships are in demand for maritime traffic safety and general security. Due to the high background noise and complex transmission channels in the marine environment, the accurate identification of ship radiation noise becomes quite complicated. Existing ship-radiated noise-based recognition systems still have some shortcomings, such as the imperfection of ship-radiated noise feature extraction and recognition algorithms, which lead to distinguishing only the type of ships rather than identifying the specific vessel. To address these issues, we propose a fine-grained ship-radiated noise recognition system that utilizes multi-scale features from the amplitude–frequency–time domain and incorporates a multi-scale feature adaptive generalized network (MFAGNet). In the feature extraction process, to cope with highly non-stationary and non-linear noise signals, the improved Hilbert–Huang transform algorithm applies the permutation entropy-based signal decomposition to perform effective decomposition analysis. Subsequently, six learnable amplitude–time–frequency features are extracted by using six-order decomposed signals, which contain more comprehensive information on the original ship-radiated noise. In the recognition process, MFAGNet is designed by applying unique combinations of one-dimensional convolutional neural networks (1D CNN) and long short-term memory (LSTM) networks. This architecture obtains regional high-level information and aggregate temporal characteristics to enhance the capability to focus on time–frequency information. The experimental results show that MFAGNet is better than other baseline methods and achieves a total accuracy of 98.89% in recognizing 12 different specific noises from ShipsEar. Additionally, other datasets are utilized to validate the universality of the method, which achieves the classification accuracy of 98.90% in four common types of ships. Therefore, the proposed method can efficiently and accurately extract the features of ship-radiated noises. These results suggest that our proposed method, as a novel underwater acoustic recognition technology, is effective for different underwater acoustic signals. Full article

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

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18 pages, 1532 KiB

Open AccessArticle

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

by Haitao Lang, Ruifu Wang, Shaoying Zheng, Siwen Wu and Jialu Li

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

Cited by 4 | Viewed by 3241

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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27 pages, 4428 KiB

Open AccessReview

Evolution of Maritime GNSS and RNSS Performance Standards

by Paweł Zalewski, Andrzej Bąk and Michael Bergmann

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

Cited by 7 | Viewed by 4540

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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