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Remote Sensing for Maritime Monitoring and Ship Surveillance
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Remote Sensing for Maritime Monitoring and Ship Surveillance

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: 25 June 2025 | Viewed by 1238

Special Issue Editors

Prof. Dr. Bingxin Liu

E-Mail Website
Guest Editor
Navigation College, Dalian Maritime University, Dalian 116026, China
Interests: marine pollution monitoring; ship detection; maritime traffic remote sensing
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Ying Li

E-Mail Website
Guest Editor
Navigation College, Dalian Maritime University, Dalian 116026, China
Interests: maritime traffic safety; marine pollution monitoring and response; smart navigation

Special Issue Information

Dear Colleagues,

In the concept of maritime traffic safety, the three key elements—humans, ships, and the navigational environment—are indispensable. Ensuring maritime traffic safety means safeguarding the safety of people, ships, and the navigational environment. With the continuous advancements in spaceborne, airborne, ship-based, and shore-based remote sensing technologies, significant progress has been made in the timeliness of data acquisition and the level of data refinement. Remote sensing has demonstrated great potential in applications related to ships and the navigational environment, specifically in maritime traffic situation awareness, ship pollution monitoring, ship surveillance, and navigation assistance. This Special Issue aims at collecting the latest theoretical, experimental, modeling, or application results in the field of remote sensing for maritime monitoring and ship surveillance. Contributions may also include case studies, review articles, or short communications.

Prof. Dr. Bingxin Liu
Prof. Dr. Ying Li
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. 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 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.

Keywords

  • marine pollution monitoring
  • ship detection
  • maritime traffic situation awareness
  • smart navigation
  • multi-source remote sensing
  • remotely sensed image process and analysis

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Published Papers (3 papers)

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Research

17 pages, 15281 KiB  
Article
Oil Film Detection for Marine Radar Image Using SBR Feature and Adaptive Threshold
by Yulong Yang, Jin Yan, Jin Xu, Xinqi Zhong, Yumiao Huang, Jianxun Rui, Min Cheng, Yuanyuan Huang, Yimeng Wang, Tao Liang, Zisen Lin and Peng Liu
J. Mar. Sci. Eng. 2025, 13(6), 1178; https://doi.org/10.3390/jmse13061178 - 16 Jun 2025
Abstract
Marine oil spills pose a serious and persistent threat to marine ecosystems, coastal resources, and global environmental health. These incidents not only disrupt ecological balance by damaging marine flora and fauna but also lead to long-term economic consequences for fisheries, tourism, and maritime [...] Read more.
Marine oil spills pose a serious and persistent threat to marine ecosystems, coastal resources, and global environmental health. These incidents not only disrupt ecological balance by damaging marine flora and fauna but also lead to long-term economic consequences for fisheries, tourism, and maritime industries. Owing to their rapid spread and often unpredictable occurrence, timely and accurate detection is essential for effective containment and mitigation. An efficient detection system can significantly enhance the responsiveness of emergency teams, enabling targeted interventions that minimize ecological damage and economic loss. This paper proposes a marine radar-based oil spill detection method that combines the Significance-to-Boundary Ratio (SBR) feature with an improved Sauvola adaptive thresholding algorithm. The raw radar data was firstly preprocessed through mean and median filtering, grayscale correction, and contrast enhancement. SBR features were then employed to extract coarse oil spill regions, which were further refined using an improved Sauvola thresholding algorithm followed by a denoising step to obtain fine-grained segmentation. Comparative experiments using different threshold values demonstrate that the proposed method achieves superior segmentation performance by better preserving oil spill boundaries and reducing background noise. Overall, the approach provides a robust and efficient solution for marine oil spill detection and monitoring. Full article
(This article belongs to the Special Issue Remote Sensing for Maritime Monitoring and Ship Surveillance)
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15 pages, 4479 KiB  
Article
Hue Angle-Based Remote Sensing of Secchi Disk Depth Using Sentinel-3 OLCI in the Coastal Waters of Qinhuangdao, China
by Yongwei Huo, Sufang Zhao, Zhongjie Yuan, Xiang Wang and Lin Wang
J. Mar. Sci. Eng. 2025, 13(6), 1149; https://doi.org/10.3390/jmse13061149 - 10 Jun 2025
Viewed by 220
Abstract
Seawater transparency provides critical insight into marine ecological dynamics and serves as a foundational indicator for fisheries management, environmental monitoring, and coastal resource development. Among various indicators, the Secchi disk depth (SDD) is widely used to quantify seawater transparency in marine environmental monitoring. [...] Read more.
Seawater transparency provides critical insight into marine ecological dynamics and serves as a foundational indicator for fisheries management, environmental monitoring, and coastal resource development. Among various indicators, the Secchi disk depth (SDD) is widely used to quantify seawater transparency in marine environmental monitoring. This study develops a remote sensing inversion model for estimating the SDD in the coastal waters of Qinhuangdao, utilizing Sentinel-3 OLCI satellite imagery and in situ measurements. The model is based on the CIE hue angle and demonstrates high accuracy (R2 = 0.93, MAPE = 7.88%, RMSE = 0.25 m), outperforming traditional single-band, band-ratio, and multi-band approaches. Using the proposed model, we analyzed the monthly and interannual variations of SDD in Qinhuangdao’s coastal waters from 2018 to 2024. The results reveal a clear seasonal pattern, with SDD values generally increasing and then decreasing throughout the year, primarily driven by the East Asian monsoon and other natural factors. Notably, the average annual SDD in 2018 was significantly lower than in subsequent years (2019–2024), which is closely associated with comprehensive water management and pollution reduction initiatives in the Bohai Sea region. These findings highlight marked improvements in the coastal marine environment and underscore the benefits of China’s ecological civilization strategy, particularly the principle that “lucid waters and lush mountains are invaluable assets.” Full article
(This article belongs to the Special Issue Remote Sensing for Maritime Monitoring and Ship Surveillance)
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28 pages, 3564 KiB  
Article
CIDNet: A Maritime Ship Detection Model Based on ISAR Remote Sensing
by Fei Liu, Boyang Liu, Hang Zhou, Song Han, Kunlin Zou, Wenjie Lv and Chang Liu
J. Mar. Sci. Eng. 2025, 13(5), 954; https://doi.org/10.3390/jmse13050954 - 14 May 2025
Viewed by 265
Abstract
Inverse synthetic aperture radar (ISAR) ship target detection is of great significance and has broad application prospects in scenarios such as protecting marine resources and maintaining maritime order. Existing ship target detection techniques, especially target detection methods and detection models in complex settings, [...] Read more.
Inverse synthetic aperture radar (ISAR) ship target detection is of great significance and has broad application prospects in scenarios such as protecting marine resources and maintaining maritime order. Existing ship target detection techniques, especially target detection methods and detection models in complex settings, have problems such as long inference time and unstable robustness, meaning that they can easily miss the best time for detecting ship targets and cause intelligence loss. To solve these problems, this study proposes a new ISAR target detection model for ships based on deep learning—Complex ISAR Detection Net (CIDNet). The model is based on the Boundary Box Efficient Transformer (BETR) architecture, which combines super-resolution preprocessing, a deep feature extraction network, a feature fusion technique, and a coordinate maintenance mechanism to improve the detection accuracy and real-time performance of ship targets in complex settings. The CIDNet improves the resolution of the input image via the super-resolution preprocessing technique, which enhances the rendering of details of ship targets in the image. The feature extraction part of the model combines the efficient feature extraction capability of YOLOv10 with the global attention mechanism of BETR. It efficiently combines information from different scales and levels through a feature fusion strategy. In addition, the model integrates a coordinated attention mechanism to enhance the focus on the target region and optimize the detection accuracy. The experimental results show that CIDNet exhibits stable performance on the test dataset. Compared with existing models such as YOLOv10 and Faster R-CNN, CIDNet improves precision, recall, and the F1 score, especially when dealing with smaller targets and complex background conditions. In addition, CIDNet achieves a detection frame rate of 63, demonstrating its fine real-time processing capabilities. Full article
(This article belongs to the Special Issue Remote Sensing for Maritime Monitoring and Ship Surveillance)
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