remotesensing-logo

Journal Browser

Journal Browser

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: 20 October 2024 | Viewed by 3440

Special Issue Editors


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

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

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

Published Papers (3 papers)

Order results
Result details
Select all
Export citation of selected articles as:

Research

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 - 09 Apr 2024
Viewed by 291
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
Show Figures

Figure 1

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 - 09 Jan 2024
Viewed by 1632
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
Show Figures

Figure 1

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 1 | Viewed by 910
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
Show Figures

Figure 1

Back to TopTop