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Advances in Marine Applications of Synthetic Aperture Radar (SAR)

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A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Remote Sensors".

Deadline for manuscript submissions: closed (20 December 2020) | Viewed by 13784

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Special Issue Editors

Dr. José C.B. da Silva

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Guest Editor

Department of Geoscience, Environment and Spatial Planning (DGAOT), Faculty of Science, University of Porto, Rua do Campo Alegre, 687, 4169-007 Porto, Portugal
Interests: satellite oceanography; ocean remote sensing; submesoscale dynamics; internal waves; river plumes; ocean color
Special Issues, Collections and Topics in MDPI journals

Mr. Benjamin M. Holt

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Guest Editor

Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA 91109, USA
Interests: the state of polar sea ice using multi-sensor data and new instrument development for microwave measurement of sea ice thickness; coastal oceanography using multiple sensors; detection of marine pollution using synthetic aperture radar

Dr. Joao A. Lorenzzetti

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Guest Editor

Remote Sensing Division, National Institute for Space Research, São José dos Campos, São Paulo 12227-010, Brazil
Interests: physical oceanography; satellite oceanography; use of synthetic aperture radar data for ocean studies and monitoring

Special Issue Information

Dear Colleagues,

Microwave imaging radars, such as day-and-night/all-weather sensors, provide a unique and detailed view of the highly variable sea surface roughness. Side-looking SAR onboard aircraft, unmanned aerial vehicles (UAVs) and space platforms, as well as new nadir-looking SAR altimeters and InSAR sensors are sensitive to a variety of sea surface signatures of ocean and atmospheric phenomena. Furthermore, geophysical measurements such as wind speed, wave properties, and Doppler-derived currents are now retrievable from many of these SAR sensors. In this Special Issue of Sensors we will collect articles covering many aspects of SAR related to science/research, algorithm/technical development, analysis tools, synergy with sensors in multiple wavelengths of the e.m. spectrum, synergy with in situ measurements at or near the sea surface interface, as well as reviews of the state-of-the-art in ocean processes using SAR imagery for ocean and sea ice monitoring.

Specifically, some of the issues we aim to address in this Special Issue of Sensors are:

Synergies between satellite SAR sensors with airborne platforms; multiple satellite optical and thermal infrared sensors including finer resolution sensors, for example Landsat-8 and Sentinel-2, etc., and in situ measurements.
Marine slicks detected by SAR, associated with biogenic surfactants, mineral oil films, and other discharged pollutants, including synergistic observations using sun glitter and optical imagers.
Advances in SAR data processing for the estimation of surface wave spectra and statistical parameters and their assimilation in wave models.
SAR measurements from autonomous vehicles.
Sea-ice observations and derived products.
Small scale ocean features such as internal waves, sub-mesoscale eddies and convergence features, rain-cells and atmospheric turbulence, river and estuarine fronts, large scale ocean fronts, etc.
Use of multiple frequencies and polarizations to interpret and quantitatively assess various surface ocean and sea ice phenomena.
Interferometric and Doppler-derived SAR oceanic and sea ice applications focused on surface motion.
Nadir-looking SAR altimeters and their new and unique applications.
New SAR ocean-related technologies and mission concepts.
Validation studies for SAR ocean and sea-ice parameters based on in situ and airborne data collections.
Advanced SAR scattering modeling of ocean and sea-ice parameters.
Vessel detection.
Iceberg tracking.

Dr. José C.B. da Silva
Mr. Benjamin M. Holt
Dr. Joao A. Lorenzzetti
Guest Editors

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Research

18 pages, 3111 KiB

Open AccessArticle

Estimating Energy Dissipation Rate from Breaking Waves Using Polarimetric SAR Images

by Rafael D. Viana, João A. Lorenzzetti, Jonas T. Carvalho and Ferdinando Nunziata

Sensors 2020, 20(22), 6540; https://doi.org/10.3390/s20226540 - 16 Nov 2020

Cited by 12 | Viewed by 2147

Abstract

The total energy dissipation rate on the ocean surface,

ϵ_{t}

(W m

^{- 2}

The total energy dissipation rate on the ocean surface,

ϵ_{t}

(W m

^{- 2}

), provides a first-order estimation of the kinetic energy input rate at the ocean–atmosphere interface. Studies on the spatial and temporal distribution of the energy dissipation rate are important for the improvement of climate and wave models. Traditional oceanographic research normally uses remote measurements (airborne and platforms sensors) and in situ data acquisition to estimate

ϵ_{t}

; however, those methods cover small areas over time and are difficult to reproduce especially in the open oceans. Satellite remote sensing has proven the potential to estimate some parameters related to breaking waves on a synoptic scale, including the energy dissipation rate. In this paper, we use polarimetric Synthetic Aperture Radar (SAR) data to estimate

ϵ_{t}

under different wind and sea conditions. The used methodology consisted of decomposing the backscatter SAR return in terms of two contributions: a polarized contribution, associated with the fast response of the local wind (Bragg backscattering), and a non-polarized (NP) contribution, associated with wave breaking (Non-Bragg backscattering). Wind and wave parameters were estimated from the NP contribution and used to calculate

ϵ_{t}

from a parametric model dependent of these parameters. The results were analyzed using wave model outputs (WAVEWATCH III) and previous measurements documented in the literature. For the prevailing wind seas conditions, the

ϵ_{t}

estimated from pol-SAR data showed good agreement with dissipation associated with breaking waves when compared to numerical simulations. Under prevailing swell conditions, the total energy dissipation rate was higher than expected. The methodology adopted proved to be satisfactory to estimate the total energy dissipation rate for light to moderate wind conditions (winds below 10 m s

^{- 1}

), an environmental condition for which the current SAR polarimetric methods do not estimate

ϵ_{t}

properly. Full article

(This article belongs to the Special Issue Advances in Marine Applications of Synthetic Aperture Radar (SAR))

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12 pages, 6537 KiB

Open AccessArticle

Time-Varying Kelvin Wake Model and Microwave Velocity Observation

by Jie Niu, Xingdong Liang and Xin Zhang

Sensors 2020, 20(6), 1575; https://doi.org/10.3390/s20061575 - 12 Mar 2020

Cited by 4 | Viewed by 2757

Abstract

In the synthetic aperture radar (SAR) imaging of ship-induced wakes, it is difficult to obtain the Doppler velocity of a Kelvin wake due to the lack of time-varying wake models and suitable radar equipment. The conventional Kelvin wake investigation based on the static Kelvin wake model failed to reflect time-varying characteristics, which are significant in the application of the Kelvin wake model. Therefore, a time-varying Kelvin wake model with consideration of geometric time-varying characteristics and the hydrodynamic equation is proposed in this paper, which reflects the wake’s time-varying change lacking in the conventional Kelvin wake investigation. The Doppler velocity measurement, measured by a specially designed radar, can be exploited to verify the time-varying model by the comparison of velocity fields. Ground-based multi-input multi-output (MIMO) millimeter wave radar imaging through the simultaneous switching of transceiver channels was used to obtain the Doppler velocity for the first time. Finally, promising results have been achieved, which are in good agreement with our proposed model in consideration of the experimental scene. The proposed time-varying model and radar equipment provide velocity measurements for the Kelvin wake observation, which contains huge application potential. Full article

(This article belongs to the Special Issue Advances in Marine Applications of Synthetic Aperture Radar (SAR))

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14 pages, 7409 KiB

Open AccessArticle

Nonuniformly-Rotating Ship Refocusing in SAR Imagery Based on the Bilinear Extended Fractional Fourier Transform

by Zhenru Pan, Huaitao Fan and Zhimin Zhang

Sensors 2020, 20(2), 550; https://doi.org/10.3390/s20020550 - 19 Jan 2020

Cited by 6 | Viewed by 2365

Abstract

Nonuniformly-rotating ship refocusing is very significant in the marine surveillance of satellite synthetic aperture radar (SAR). The majority of ship imaging algorithms is based on the inverse SAR (ISAR) technique. On the basis of the ISAR technique, several parameter estimation algorithms were proposed for nonuniformly rotating ships. But these algorithms still have problems on cross-terms and noise suppression. In this paper, a refocusing algorithm for nonuniformly rotating ships based on the bilinear extended fractional Fourier transform (BEFRFT) is proposed. The ship signal in a range bin can be modeled as a multicomponent cubic phase signal (CPS) after motion compensation. BEFRFT is a bilinear extension of fractional Fourier transform (FRFT), which can estimate the chirp rates and quadratic chirp rates of CPSs. Furthermore, BEFRFT has excellent performances on cross-terms and noise suppression. The results of simulated data and Gaofen-3 data verify the effectiveness of BEFRFT. Full article

(This article belongs to the Special Issue Advances in Marine Applications of Synthetic Aperture Radar (SAR))

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16 pages, 15641 KiB

Open AccessArticle

Multifeature Fusion Neural Network for Oceanic Phenomena Detection in SAR Images

by Zhuofan Yan, Jinsong Chong, Yawei Zhao, Kai Sun, Yuhang Wang and Yan Li

Sensors 2020, 20(1), 210; https://doi.org/10.3390/s20010210 - 30 Dec 2019

Cited by 11 | Viewed by 2915

Abstract

Oceanic phenomena detection in synthetic aperture radar (SAR) images is important in the fields of fishery, military, and oceanography. The traditional detection methods of oceanic phenomena in SAR images are based on handcrafted features and detection thresholds, which have a problem of poor generalization ability. Methods based on deep learning have good generalization ability. However, most of the deep learning methods currently applied to oceanic phenomena detection only detect one type of phenomenon. To satisfy the requirements of efficient and accurate detection of multiple information of multiple oceanic phenomena in massive SAR images, this paper proposes an oceanic phenomena detection method in SAR images based on convolutional neural network (CNN). The method first uses ResNet-50 to extract multilevel features. Second, it uses the atrous spatial pyramid pooling (ASPP) module to extract multiscale features. Finally, it fuses multilevel features and multiscale features to detect oceanic phenomena. The SAR images acquired from the Sentinel-1 satellite are used to establish a sample dataset of oceanic phenomena. The method proposed can achieve 91% accuracy on the dataset. Full article

(This article belongs to the Special Issue Advances in Marine Applications of Synthetic Aperture Radar (SAR))

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22 pages, 12984 KiB

Open AccessArticle

Marine Oil Slick Detection Based on Multi-Polarimetric Features Matching Method Using Polarimetric Synthetic Aperture Radar Data

by Guannan Li, Ying Li, Bingxin Liu, Peng Wu and Chen Chen

Sensors 2019, 19(23), 5176; https://doi.org/10.3390/s19235176 - 26 Nov 2019

Cited by 8 | Viewed by 2480

Abstract

Polarimetric synthetic aperture radar is an important tool in the effective detection of marine oil spills. In this study, two cases of Radarsat-2 Fine mode quad-polarimetric synthetic aperture radar datasets are exploited to detect a well-known oil seep area that collected over the Gulf of Mexico using the same research area, sensor, and time. A novel oil spill detection scheme based on a multi-polarimetric features model matching method using spectral pan-similarity measure (SPM) is proposed. A multi-polarimetric features curve is generated based on optimal polarimetric features selected using Jeffreys–Matusita distance considering its ability to discriminate between thick and thin oil slicks and seawater. The SPM is used to search for and match homogeneous unlabeled pixels and assign them to a class with the highest similarity to their spectral vector size, spectral curve shape, and spectral information content. The superiority of the SPM for oil spill detection compared to traditional spectral similarity measures is demonstrated for the first time based on accuracy assessments and computational complexity analysis by comparing with four traditional spectral similarity measures, random forest (RF), support vector machine (SVM), and decision tree (DT). Experiment results indicate that the proposed method has better oil spill detection capability, with a higher average accuracy and kappa coefficient (1.5–7.9% and 1–25% higher, respectively) than the four traditional spectral similarity measures under the same computational complexity operations. Furthermore, in most cases, the proposed method produces valuable and acceptable results that are better than the RF, SVM, and DT in terms of accuracy and computational complexity. Full article

(This article belongs to the Special Issue Advances in Marine Applications of Synthetic Aperture Radar (SAR))

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