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Added-Value SAR Products for the Observation of Coastal Areas

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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 (15 December 2022) | Viewed by 12248

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Special Issue Editors

Dr. Andrea Buono

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Guest Editor

Engineering Department, University of Naples “Parthenope”, 80143 Naples, Italy
Interests: electromagnetic modeling; SAR; polarimetry; ocean; coastal areas
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Weizeng Shao

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Guest Editor

College of Marine Sciences, Shanghai Ocean University, Shanghai 201306, China
Interests: SAR; tropical cyclone; numeric modeling
Special Issues, Collections and Topics in MDPI journals

Dr. Carina Regina de Macedo

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Guest Editor

Laboratoire d'Océanologie et de Géosciences (LOG), French National Centre for Scientific Research, 62930 Wimereux, France
Interests: electromagnetic modeling; SAR; polarimetry; ocean mesoscale features

Special Issue Information

Dear Colleagues,

Coastal regions represent extremely important areas for the economy, environment and social life. In Europe, about 40% of people live within 50 km of the sea while almost 40% of gross production comes from those regions, which corresponds to approximately 75% of the maritime export volume. Coastal anthropogenic activities such as shipping, resource exploration and extraction, tourism, renewable energy are of paramount importance for human life while putting pressure on local ecosystems that need to be preserved.

Hence, a continuous, updated and synoptic monitoring and mapping of those activities and the impact they have on the coastal ecosystems, even at polar regions, through processes as pollution, coastal erosion and climate change is needed. Within this framework, the use of multi-platform/frequency/polarization synthetic aperture radar (SAR) as well other microwave satellites can provide regional-scale and fine-resolution day and night observations of coastal areas under almost all weather conditions. Currently, the spreading of SAR imagery worldwide has enabled new applications and boosted the development of new models and algorithms to generate added-value products.

Accordingly, in this Special Issue, although the exploitation of SAR measurements for the observation of coastal areas is a well-established methodology, we are seeking the latest research advancements on related topics, including innovative methods, improved models and analysis tools and new added-value products. Hence, the topics of this Special Issue include, but are not limited to, the following subjects:

Land use/land cover classification;
Extraction and analysis of the coastal profile;
Monitoring of anthropogenic targets and critical infrastructures as oil/gas fields, harbors, ships, offshore wind farms, aquaculture, plastic litter;
Estimation of sea wind field;
Detection of natural targets as icebergs, algal blooms, oil spills, mangroves;
Modeling and retrieval of geophysical parameters.

Dr. Andrea Buono
Prof. Dr. Weizeng Shao
Dr. Carina Regina de Macedo
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

SAR
target detection
classification
ocean
wind
modeling
retrieval
polarimetry

Published Papers (5 papers)

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Research

30 pages, 7300 KiB

Open AccessArticle

Development and Application of Predictive Models to Distinguish Seepage Slicks from Oil Spills on Sea Surfaces Employing SAR Sensors and Artificial Intelligence: Geometric Patterns Recognition under a Transfer Learning Approach

by Patrícia Carneiro Genovez, Francisco Fábio de Araújo Ponte, Ítalo de Oliveira Matias, Sarah Barrón Torres, Carlos Henrique Beisl, Manlio Fernandes Mano, Gil Márcio Avelino Silva and Fernando Pellon de Miranda

Remote Sens. 2023, 15(6), 1496; https://doi.org/10.3390/rs15061496 - 08 Mar 2023

Cited by 1 | Viewed by 1656

Abstract

The development and application of predictive models to distinguish seepage slicks from oil spills are challenging, since Synthetic Aperture Radars (SAR) detect these events as dark spots on the sea surface. Traditional Machine Learning (ML) has been used to discriminate the Oil Slick Source (OSS) as natural or anthropic assuming that the samples employed to train and test the models in the source domain (D_S) follow the same statistical distribution of unknown samples to be predicted in the target domain (D_T). When such assumptions are not held, Transfer Learning (TL) allows the extraction of knowledge from validated models and the prediction of new samples, thus improving performances even in scenarios never seen before. A database with 26 geometric features extracted from 6279 validated oil slicks was used to develop predictive models in the Gulf of Mexico (GoM) and its Mexican portion (GMex). Innovatively, these well-trained models were applied to predict the OSS of unknown events in the GoM, the American (GAm) portion of the GoM, and in the Brazilian continental margin (BR). When the D_S and D_T domains are similar, the TL and generalization are null, being equivalent to the usual ML. However, when domains are different but statically related, TL outdoes ML (58.91%), attaining 87% of global accuracy when using compatible SAR sensors in the D_S and D_T domains. Conversely, incompatible SAR sensors produce domains statistically divergent, causing negative transfers and generalizations. From an operational standpoint, the evidenced generalization capacity of these models to recognize geometric patterns across different geographic regions using TL may allow saving time and budget, avoiding the collection of validated and annotated new training samples, as well as the models re-training from scratch. When looking for new exploratory frontiers, automatic prediction is a value-added product that strengthens the knowledge-driven classifications and the decision-making processes. Moreover, the prompt identification of an oil spill can speed up the response actions to clean up and protect sensitive areas against oil pollution. Full article

(This article belongs to the Special Issue Added-Value SAR Products for the Observation of Coastal Areas)

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22 pages, 10479 KiB

Open AccessArticle

On the Effects of the Incidence Angle on the L-Band Multi-Polarisation Scattering of a Small Ship

by Muhammad Adil, Andrea Buono, Ferdinando Nunziata, Emanuele Ferrentino, Domenico Velotto and Maurizio Migliaccio

Remote Sens. 2022, 14(22), 5813; https://doi.org/10.3390/rs14225813 - 17 Nov 2022

Cited by 12 | Viewed by 1866

Abstract

The monitoring of ships is of paramount importance for ocean and coastal area surveillance. The synthetic aperture radar is shown to be a key sensor to provide effective and continuous observation of ships due to its unique imaging capabilities. When advanced synthetic aperture radar imaging systems are considered, the full scattering information is available that was demonstrated to be beneficial in developing improved ship detection and classification algorithms. Nonetheless, the capability of polarimetric synthetic aperture radar to observe marine vessels is significantly affected by several imaging and environmental parameters, including the incidence angle. Nonetheless, how changes in the incidence angle affect the scattering of ships still needs to be further investigated since only a sparse analysis, i.e., on different kinds of ships of different sizes observed at multiple incidence angles, has been performed. Hence, in this study, for the first time, the polarimetric scattering of the same ship, i.e., a small fishing trawler, which is imaged multiple times under the same sea state conditions but in a wide range of incidence angles, is analysed. This unique opportunity is provided by a premium L-band UAVSAR airborne dataset that consists of five full-polarimetric synthetic aperture radar scenes collected in the Gulf of Mexico. Experimental results highlight the key role played by the incidence angle on both coherent, i.e., co-polarisation signature and pedestal height, and incoherent, i.e., multi-polarisation and total backscattering power, polarimetric scattering descriptors. Experimental results show that: (1) the polarised scattering component is more sensitive to the incidence angle with respect to the unpolarised one; (2) the co-polarised channel under horizontal polarisation dominated the polarimetric backscattering from the fishing trawler at lower angles of incidence, while both co-polarised channels contribute to the polarimetric backscattering at higher incidence angles; (3) the HV polarisation provides the largest target-to-clutter ratio at lower incidence angles, while the HH polarisation should be preferred at higher angles of incidence. Full article

(This article belongs to the Special Issue Added-Value SAR Products for the Observation of Coastal Areas)

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26 pages, 18654 KiB

Open AccessArticle

Offshore Oil Platform Detection in Polarimetric SAR Images Using Level Set Segmentation of Limited Initial Region and Convolutional Neural Network

by Chun Liu, Jian Yang, Jianghong Ou and Dahua Fan

Remote Sens. 2022, 14(7), 1729; https://doi.org/10.3390/rs14071729 - 03 Apr 2022

Cited by 6 | Viewed by 2143

Abstract

Offshore oil platforms are difficult to detect due to the complex sea state, the sparseness of target distribution, and the similarity of targets with ships. In this paper, we propose an oil platform detection method in polarimetric synthetic aperture radar (PolSAR) images using level set segmentation of a limited initial region and a convolutional neural network (CNN). Firstly, to reduce the interference of sea clutter, the offshore strong scattering targets were initially detected by the generalized optimization of polarimetric contrast enhancement (GOPCE) detector. Secondly, to accurately locate the contour of targets and eliminate false alarms, the coarse results were refined using an improved level set segmentation method. An algorithm for splitting and merging the smallest enclosing circle (SMSEC) was proposed to cover the coarse results and obtain the initial level set function. Finally, the LeNet-5 CNN model was used to classify the oil platforms and ships. Experimental results using multiple sets of polarimetric SAR data acquired by RADARSAT-2 show that the performance of the proposed method, including the detection rate, the false alarm rate, and the Intersection over Union (IOU) index between the extracted ROI and the ground truth, is better than the performance of a method that combines a GOPCE detector and a support vector machine classifier. Full article

(This article belongs to the Special Issue Added-Value SAR Products for the Observation of Coastal Areas)

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20 pages, 11852 KiB

Open AccessArticle

Assessment of Ocean Swell Height Observations from Sentinel-1A/B Wave Mode against Buoy In Situ and Modeling Hindcasts

by He Wang, Alexis Mouche, Romain Husson, Antoine Grouazel, Bertrand Chapron and Jingsong Yang

Remote Sens. 2022, 14(4), 862; https://doi.org/10.3390/rs14040862 - 11 Feb 2022

Cited by 11 | Viewed by 2687

Abstract

Synthetic Aperture Radar (SAR) in wave mode is a powerful tool for monitoring sea states in terms of long-period ocean swells of a specific wave directional partition. Since 2016, SARs aboard Sentinel-1A/B operating in wave mode have provided ocean swell spectra dataset as Level-2 Ocean products on a continuous and global basis over open oceans. Furthermore, Level-3 swell products are processed by Copernicus Marine Environment Monitoring Services (CMEMS) taking the benefit of the unique “fireworks” analysis. In this paper, swell wave heights from Sentinel-1A/B wave mode during the period from June 2016 to June 2020 are evaluated. The reference data include the collocated in situ measurements from directional wave buoys and WaveWatch III (WW3) hindcasts. Assessment results show systematic overestimation of approximately 0.2 m in terms of the partitioned swell heights for Sentinel-1A/B Level-2 products compared to the directional buoy observations in eastern Pacific and the western Atlantic. Based on the reliable SAR-WW3 collocations after quality-controls, empirical corrections have been proposed for Sentinel-1 Level-2 swell heights. Independent comparisons against WW3 hindcasts and buoy observations demonstrate the validity of our postprocessing correction for both Level-2 and Level-3 swell heights by eliminating the biases and reducing the root mean square errors. The consistency between CMEMS Level-3 swells and buoy in situ is also examined and discussed by case studies. Full article

(This article belongs to the Special Issue Added-Value SAR Products for the Observation of Coastal Areas)

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19 pages, 43835 KiB

Open AccessArticle

Application of SAR Data for Tropical Cyclone Intensity Parameters Retrieval and Symmetric Wind Field Model Development

by Yuan Gao, Jie Zhang, Jian Sun and Changlong Guan

Remote Sens. 2021, 13(15), 2902; https://doi.org/10.3390/rs13152902 - 23 Jul 2021

Cited by 4 | Viewed by 2296

Abstract

The spaceborne synthetic aperture radar (SAR) is an effective tool to observe tropical cyclone (TC) wind fields at very high spatial resolutions. TC wind speeds can be retrieved from cross-polarization signals without wind direction inputs. This paper proposed methodologies to retrieve TC intensity parameters; for example, surface maximum wind speed, TC fullness (TCF) and central surface pressure from the European Space Agency Sentinel-1 Extra Wide swath mode cross-polarization data. First, the MS1A geophysical model function was modified from 6 to 69 m/s, based on three TC samples’ SAR images and the collocated National Oceanic and Atmospheric Administration stepped frequency microwave radiometer wind speed measurements. Second, we retrieved the wind fields and maximum wind speeds of 42 TC samples up to category 5 acquired in the last five years, using the modified MS1A model. Third, the TCF values and central surface pressures were calculated from the 1-km wind retrievals, according to the radial curve fitting of wind speeds and two hurricane wind-pressure models. Three intensity parameters were found to be dependent upon each other. Compared with the best-track data, the averaged bias, correlation coefficient (Cor) and root mean-square error (RMSE) of the SAR-retrieved maximum wind speeds were –3.91 m/s, 0.88 and 7.99 m/s respectively, showing a better result than the retrievals before modification. For central pressure, the averaged bias, Cor and RMSE were 1.17 mb, 0.77 and 21.29 mb and respectively, indicating the accuracy of the proposed methodology for pressure retrieval. Finally, a new symmetric TC wind field model was developed with the fitting function of the TCF values and maximum wind speeds, radial wind curve and the Rankine Vortex model. By this model, TC wind field can be simulated just using the maximum wind speed and the radius of maximum wind speed. Compared with wind retrievals, averaged absolute bias and averaged RMSE of all samples’ wind fields simulated by the new model were smaller than those of the Rankine Vortex model. Full article

(This article belongs to the Special Issue Added-Value SAR Products for the Observation of Coastal Areas)

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