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Editorial Board Members’ Collection Series — Recent Advances in Ocean Radar
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Editorial Board Members’ Collection Series — Recent Advances in Ocean Radar

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

Deadline for manuscript submissions: closed (20 October 2024) | Viewed by 7734

Special Issue Editors

Dr. Martin Gade

E-Mail Website
Guest Editor
Institut für Meereskunde, Universität Hamburg, Bundesstraße 53, 20146 Hamburg, Germany
Interests: coastal remote sensing; SAR; marine surface films; ocean radar backscattering; air-sea interactions; air-sea fluxes
Special Issues, Collections and Topics in MDPI journals
Dr. Ferdinando Nunziata

E-Mail Website
Guest Editor
Dipartimento di Ingegneria, Università di Napoli Parthenope, 80133 Napoli, NA, Italy
Interests: synthetic aperture radar for sea observation; microwave radiometry; sea surface scattering; GNSS reflectometry
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Weimin Huang

E-Mail Website
Guest Editor
Department of Electrical and Computer Engineering, Memorial University, St. John’s, NL A1B 3X5, Canada
Interests: mapping of oceanic surface parameters via high-frequency ground wave radar; X-band marine radar and global navigation satellite systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Radar technologies are widely used to observe marine environments, due to their ability to provide all-day and almost all-weather measurements at a spatial coverage and to revisit time, while coping with the requirements of a broad range of marine and maritime applications, spanning from coastal monitoring up to the estimation of ocean variables. This makes radar technologies attractive both scientifically (to foster the development of new models and methods that allow a better understanding of marine environments and their evolution) and operationally (to provide value-added products of paramount importance for policy makers and end users).

The purpose of this Special Issue is to provide a platform for researchers involved in marine/maritime radar applications to present their latest advances in the field, to discuss the improvements stemming from new methods/models with respect to the state of the art in the field, and to present new opportunities that arise from the joint combination of radar measurements performed on different platforms (e.g.; satellites and drones) and from the synergistic use of radar and optical observations of the world’s oceans.

This topic collection on the "Recent Advances in Ocean Radars" aims to share new theoretical and experimental work on ocean radar concepts, techniques, and applications. The topics of interest may include, but are not limited to, the following:

  • High-frequency (HF) radars;
  • New progress in ocean synthetic aperture radars;
  • Studies on ocean surface scattering and roughness;
  • Ocean winds and currents;
  • Coastal erosion and inland classification of radar imagery;
  • Marine target detection;
  • Marine pollution observation;
  • Deep learning/machine learning/artificial intelligence for radar applications.

Dr. Martin Gade
Dr. Ferdinando Nunziata
Prof. Dr. Weimin Huang
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

  • radar for ocean applications
  • synthetic aperture radar
  • HF radar
  • marine pollution
  • ship detection
  • ocean winds
  • ocean currents
  • coastal observation
  • ocean surface scattering

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Further information on MDPI's Special Issue polices can be found here.

Published Papers (6 papers)

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Research

23 pages, 13107 KiB  
Article
Improved Polar Current Shell Algorithm for Ocean Current Retrieval from X-Band Radar Data
by Yi Li, Zhiding Yang and Weimin Huang
Remote Sens. 2024, 16(22), 4140; https://doi.org/10.3390/rs16224140 - 6 Nov 2024
Viewed by 458
Abstract
This paper presents an improved algorithm for retrieving ocean surface currents from X-band marine radar images. The original polar current shell (PCS) method begins with a 3D fast Fourier transform (FFT) of the radar image sequence, followed by the extraction of the dispersion [...] Read more.
This paper presents an improved algorithm for retrieving ocean surface currents from X-band marine radar images. The original polar current shell (PCS) method begins with a 3D fast Fourier transform (FFT) of the radar image sequence, followed by the extraction of the dispersion shell from the 3D image spectrum, which is then transformed into a PCS using polar coordinates. Building on this foundation, the improved approach is to analyze all data points corresponding to different wavenumber magnitudes in the PCS domain rather than analyzing each specific wavenumber magnitude separately. In addition, kernel density estimation (KDE) to identify high-density directions, interquartile range filtering to remove outliers, and symmetry-based filtering to further reduce noise by comparing data from opposite directions are also utilized for further improvement. Finally, a single curve fitting is applied to the filtered data rather than conducting multiple curve fittings as in the original method. The algorithm is validated using simulated data and real radar data from both the Decca radar, established in 2008, and the Koden radar, established in 2017. For the 2008 Decca radar data, the improved PCS method reduced the root-mean-square deviation (RMSD) for speed estimation by 0.06 m/s and for direction estimation by 3.8° while improving the correlation coefficients (CCs) for current speed by 0.06 and direction by 0.07 compared to the original PCS method. For the 2017 Koden radar data, the improved PCS method reduced the RMSD for speed by 0.02 m/s and for direction by 4.6°, with CCs being improved for current speed by 0.03 and direction by 0.05 compared to the original PCS method. Full article
(This article belongs to the Special Issue Editorial Board Members’ Collection Series — Recent Advances in Ocean Radar)
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27 pages, 5540 KiB  
Article
Marine Radar Constant False Alarm Rate Detection in Generalized Extreme Value Distribution Based on Space-Time Adaptive Filtering Clutter Statistical Analysis
by Baotian Wen, Zhizhong Lu and Bowen Zhou
Remote Sens. 2024, 16(19), 3691; https://doi.org/10.3390/rs16193691 - 3 Oct 2024
Viewed by 578
Abstract
The performance of marine radar constant false alarm rate (CFAR) detection method is significantly influenced by the modeling of sea clutter distribution and detector decision rules. The false alarm rate and detection rate are therefore unstable. In order to address low CFAR detection [...] Read more.
The performance of marine radar constant false alarm rate (CFAR) detection method is significantly influenced by the modeling of sea clutter distribution and detector decision rules. The false alarm rate and detection rate are therefore unstable. In order to address low CFAR detection performance and the modeling problem of non-uniform, non-Gaussian, and non-stationary sea clutter distribution in marine radar images, in this paper, a CFAR detection method in generalized extreme value distribution modeling based on marine radar space-time filtering background clutter is proposed. Initially, three-dimensional (3D) frequency wave-number (space-time) domain adaptive filter is employed to filter the original radar image, so as to obtain uniform and stable background clutter. Subsequently, generalized extreme value (GEV) distribution is introduced to integrally model the filtered background clutter. Finally, Inclusion/Exclusion (IE) with the best performance under the GEV distribution is selected as the clutter range profile CFAR (CRP-CFAR) detector decision rule in the final detection. The proposed method is verified by utilizing real marine radar image data. The results indicate that when the Pfa is set at 0.0001, the proposed method exhibits an average improvement in PD of 2.3% compared to STAF-RCBD-CFAR, and a 6.2% improvement compared to STCS-WL-CFAR. When the Pfa is set at 0.001, the proposed method exhibits an average improvement in PD of 6.9% compared to STAF-RCBD-CFAR, and a 9.6% improvement compared to STCS-WL-CFAR. Full article
(This article belongs to the Special Issue Editorial Board Members’ Collection Series — Recent Advances in Ocean Radar)
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Graphical abstract

26 pages, 6642 KiB  
Article
Performance of the Earth Explorer 11 SeaSTAR Mission Candidate for Simultaneous Retrieval of Total Surface Current and Wind Vectors
by Adrien C. H. Martin, Christine P. Gommenginger, Daria Andrievskaia, Petronilo Martin-Iglesias and Alejandro Egido
Remote Sens. 2024, 16(19), 3556; https://doi.org/10.3390/rs16193556 - 24 Sep 2024
Viewed by 900
Abstract
Interactions between ocean surface currents, winds and waves at the atmosphere-ocean interface are key controls of lateral and vertical exchanges of water, heat, carbon, gases and nutrients in the global Earth System. The SeaSTAR satellite mission concept proposes to better quantify and understand [...] Read more.
Interactions between ocean surface currents, winds and waves at the atmosphere-ocean interface are key controls of lateral and vertical exchanges of water, heat, carbon, gases and nutrients in the global Earth System. The SeaSTAR satellite mission concept proposes to better quantify and understand these important dynamic processes by measuring two-dimensional fields of total surface current and wind vectors with unparalleled spatial and temporal resolution (1 × 1 km2 or finer, 1 day) and unmatched precision over one continuous wide swath (100 km or more). This paper presents a comprehensive numerical analysis of the expected performance of the Earth Explorer 11 (EE11) SeaSTAR mission candidate in the case of idealised and realistic 2D ocean currents and wind fields. A Bayesian framework derived from satellite scatterometry is adapted and applied to SeaSTAR’s bespoke inversion scheme that simultaneously retrieves total surface current vectors (TSCV) and ocean surface vector winds (OSVW). The results confirm the excellent performance of the EE11 SeaSTAR concept, with Root Mean Square Errors (RMSE) for TSCV and OSVW at 1 × 1 km2 resolution consistently better than 0.1 m/s and 0.4 m/s, respectively. The analyses highlight some performance degradation in some relative wind directions, particularly marked at near range and low wind speeds. Retrieval uncertainties are also reported for several variations around the SeaSTAR baseline three-azimuth configuration, indicating that RMSEs improve only marginally (by ∼0.01 m/s for TSCV) when including broadside Radial Surface Velocity or broadside dual-polarisation data in the inversion. In contrast, our results underscore (a) the critical need to include broadside Normalised Radar Cross Section data in the inversion; (b) the rapid performance degradation when broadside incidence angles become steeper than 20° from nadir; and (c) the benefits of maintaining ground squint angle separation between fore and aft lines-of-sight close to 90°. The numerical results are consistent with experimental performance estimates from airborne data and confirm that the EE11 SeaSTAR concept satisfies the requirements of the mission objectives. Full article
(This article belongs to the Special Issue Editorial Board Members’ Collection Series — Recent Advances in Ocean Radar)
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17 pages, 4469 KiB  
Article
Analytical Coherent Detection in High-Resolution Dual-Polarimetric Sea Clutter with Independent Inverse Gamma Textures
by Tingyu Duan, Penglang Shui, Jianming Wang and Shuwen Xu
Remote Sens. 2024, 16(8), 1315; https://doi.org/10.3390/rs16081315 - 9 Apr 2024
Viewed by 814
Abstract
Polarization diversity has been widely used in maritime radars to improve target detection performance. Full utilization of the polarimetric characteristics of sea clutter is the key to designing effective polarimetric detectors. For high-resolution maritime radars, the HH-HV dual-polarization is an affordable and effective [...] Read more.
Polarization diversity has been widely used in maritime radars to improve target detection performance. Full utilization of the polarimetric characteristics of sea clutter is the key to designing effective polarimetric detectors. For high-resolution maritime radars, the HH-HV dual-polarization is an affordable and effective mode to monitor small targets, owing to the simple configuration of single-polarimetric transmit and dual-polarimetric reception and lower clutter powers at the HH and HV polarizations. Enlightened by the analytical coherent detector in compound-Gaussian clutter with inverse Gamma texture, this paper investigates dual-polarimetric coherent detection in dual-polarimetric compound-Gaussian clutter with independent inverse Gamma distributed textures. The analytical dual-polarimetric near-optimum coherent detector is derived, which is a fusion of the generalized likelihood ratio test linear threshold detectors (GLRT-LTDs) at the two polarizations. For short, it is referred to as the P-GLRT-LTD. It is proven that the P-GLRT-LTD is of constant false alarm rate with respect to the Doppler steering vector, scale parameters of textures, and speckle covariance matrices. Moreover, the thresholds of the P-GLRT-LTD are given analytically. Experiments using simulated sea clutter data with the estimated scale and shape parameters from the two measured intelligent pixel processing radar (IPIX) datasets and two measured IPIX datasets with test targets are made to compare P-GLRT-LTD with other existing dual-polarimetric coherent detectors. The results show that the P-GLRT-LTD attains the same detection performance as the existing best-performance detector. The P-GLRT-LTD has a lower computational cost than the existing best-performing one. Full article
(This article belongs to the Special Issue Editorial Board Members’ Collection Series — Recent Advances in Ocean Radar)
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17 pages, 8577 KiB  
Article
A Feasibility Study of Nearshore Bathymetry Estimation via Short-Range K-Band MIMO Radar
by Giovanni Ludeno, Matteo Antuono, Francesco Soldovieri and Gianluca Gennarelli
Remote Sens. 2024, 16(2), 261; https://doi.org/10.3390/rs16020261 - 9 Jan 2024
Cited by 1 | Viewed by 2330
Abstract
This paper provides an assessment of a 24 GHz multiple-input multiple-output radar as a remote sensing tool to retrieve bathymetric maps in coastal areas. The reconstruction procedure considered here exploits the dispersion relation and has been previously employed to elaborate the data acquired [...] Read more.
This paper provides an assessment of a 24 GHz multiple-input multiple-output radar as a remote sensing tool to retrieve bathymetric maps in coastal areas. The reconstruction procedure considered here exploits the dispersion relation and has been previously employed to elaborate the data acquired via X-band marine radar. The estimation capabilities of the sensor are investigated firstly on synthetic radar data. With this aim, case studies referring to sea waves interacting with a constant and a spatially varying bathymetry are both considered. Finally, the reconstruction procedure is tested by processing real data recorded at Bagnoli Bay, Naples, South Italy. The preliminary results shown here confirm the potential of the radar sensor as a tool for sea wave monitoring. Full article
(This article belongs to the Special Issue Editorial Board Members’ Collection Series — Recent Advances in Ocean Radar)
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18 pages, 6346 KiB  
Article
EddyDet: A Deep Framework for Oceanic Eddy Detection in Synthetic Aperture Radar Images
by Di Zhang, Martin Gade, Wensheng Wang and Haoran Zhou
Remote Sens. 2023, 15(19), 4752; https://doi.org/10.3390/rs15194752 - 28 Sep 2023
Cited by 3 | Viewed by 1543
Abstract
This paper presents a deep framework EddyDet to automatically detect oceanic eddies in Synthetic Aperture Radar (SAR) images. The EddyDet has been developed using the Mask Region with Convolutional Neural Networks (Mask RCNN) framework, incorporating two new branches: Edge Head and Mask Intersection [...] Read more.
This paper presents a deep framework EddyDet to automatically detect oceanic eddies in Synthetic Aperture Radar (SAR) images. The EddyDet has been developed using the Mask Region with Convolutional Neural Networks (Mask RCNN) framework, incorporating two new branches: Edge Head and Mask Intersection over Union (IoU) Head. The Edge Head can learn internal texture information implicitly, and the Mask IoU Head improves the quality of predicted masks. A SAR dataset for Oceanic Eddy Detection (SOED) is specifically constructed to evaluate the effectiveness of the EddyDet model in detecting oceanic eddies. We demonstrate that the EddyDet is capable of achieving acceptable eddy detection results under the condition of limited training samples, which outperforms a Mask RCNN baseline in terms of average precision. The combined Edge Head and Mask IoU Head have the ability to describe the characteristics of eddies more correctly, while the EddyDet shows great potential in practice use accurately and time efficiently, saving manual labor to a large extent. Full article
(This article belongs to the Special Issue Editorial Board Members’ Collection Series — Recent Advances in Ocean Radar)
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