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Special Issue "Ocean Remote Sensing with Synthetic Aperture 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 (30 September 2017)

Special Issue Editors

Guest Editor
Dr. Xiaofeng Yang

State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, 20A Datun Rd, Beijing 100101, China
Website | E-Mail
Phone: +86-10-64806215
Interests: satellite oceanography; SAR applications; marine atmospheric boundary layer process studies; marine pollution monitoring; air–sea interactions
Guest Editor
Dr. Xiaofeng Li

National Oceanic and Atmospheric Administration, NCWCP E/RA3, 5830 University Research Ct. Office #3216, College Park, MD 20740-3818, USA
Website | E-Mail
Phone: +1-301-683-3314
Interests: ocean remote sensing; physical oceanography; boundary layer meteorology; synthetic aperture radar imaging mechanism; multiple-polarization radar applications; satellite image classification and segmentation
Guest Editor
Dr. Ferdinando Nunziata

Università degli Studi di Napoli Parthenope, Dipartimento di Ingnegneria, Centro Direzionale, isola C4 -80143 Napoli Italy
Website | E-Mail
Phone: +390815476779
Interests: SAR; polarimetric SAR; target detection; marine monitoring; coastaline extraction and land classification.
Guest Editor
Dr. Alexis Mouche

Laboratoire d’Océanographie Physique et Spatiale/ , Ifremer, CS 10070 - 29280 Plouzané, France
Website | E-Mail
Phone: +33 (0)2 98 22 49 29
Interests: interactions of electromagnetic and oceanic waves; marine atmospheric boundary layer processes; remote sensing for extreme events characterization; multi-polarization radar and sensors synergy for ocean applications

Special Issue Information

Dear Colleagues,

The oceans covers approximately 71% of the Earth’s surface, 90% of the biosphere and contains 97% of Earth’s water. In 1978, NASA launched the first SeaSat satellite, primilary aiming at ocean observations and the microwave synthetic aperture radar (SAR) was one of four instruments. Since then, the global oceans have been observed on SAR images, which has a high resolution (<100 m spatial resolution) and a large swath (450 km for ScanSAR mode images). The microwave SAR can image the ocean surface in all weather conditions and day or night. An increasing number of SAR satellites have become available since the early 1990s, such as the ERS-1/-2 and Envisat satellites, the Radarsat-1/-2 satellites, the COSMO-SkyMed satellites, TerraSAR-X and TanDEM-X, among others. Recently, the European Space Agency lauched a new generation of SAR satellites (Sentinel-1A in 2014 and Sentinel-1B in 2016). This operational SAR mission, for the first time, provides researchers with free and open SAR images necessary to carry out broader and deeper investigation of the global oceans.

SAR remote sensing on ocean and coast monitoring has become a research hotspot in geoscience and remote sensing. This Special Issue on “Ocean Remote Sensing with Synthetic Aperture Radar” is focused on ocean dynamical studies of sea surface phenomena, air–sea interactions, man-made object detection and radar imaging mechanisms. We would like to invite articles on ocean-related studies using state-of-the-art SAR techniques. The topics of this Special Issue include, without being limited to, the following subjects: 

  • Ocean applications with SAR imagery (wind, wave, precipataion, etc.)
  • SAR studies of physical and biological oceanography
  • Coastline extraction and inland area classification of SAR imagery
  • Methods for ship and other man-made objects’ detection
  • Remote sensing of oceanic surface and internal waves, upwellings, bathymetry, etc.
  • Cyclone–related parameters retrieval from SAR satellite observations
  • Marine atmospheric boundary layer process studies using SAR and remotely sensed data
  • Remote sensing modelling over complex sea surfaces
  • Oil spill and seep detections with SAR
  • PolSAR and InSAR application for coastal research issues

Authors are requested to check and follow the specific Instructions to Authors, see https://dl.dropboxusercontent.com/u/165068305/Remote_Sensing-Additional_Instructions.pdf

We look forward to receiving your submissions in this interesting area of specialization.

Dr. Xiaofeng Yang
Dr. Xiaofeng Li
Dr. Ferdinando Nunziata
Dr. Alexis Mouche
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 papers will be 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 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 1600 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

  • Ocean
  • SAR
  • microwave
  • polarization
  • coastal oceanography

Published Papers (20 papers)

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Open AccessArticle Exploring the Potential of Active Learning for Automatic Identification of Marine Oil Spills Using 10-Year (2004–2013) RADARSAT Data
Remote Sens. 2017, 9(10), 1041; doi:10.3390/rs9101041
Received: 13 August 2017 / Revised: 5 October 2017 / Accepted: 9 October 2017 / Published: 13 October 2017
PDF Full-text (2020 KB) | HTML Full-text | XML Full-text
Abstract
This paper intends to find a more cost-effective way for training oil spill classification systems by introducing active learning (AL) and exploring its potential, so that satisfying classifiers could be learned with reduced number of labeled samples. The dataset used has 143 oil
[...] Read more.
This paper intends to find a more cost-effective way for training oil spill classification systems by introducing active learning (AL) and exploring its potential, so that satisfying classifiers could be learned with reduced number of labeled samples. The dataset used has 143 oil spills and 124 look-alikes from 198 RADARSAT images covering the east and west coasts of Canada from 2004 to 2013. Six uncertainty-based active sample selecting (ACS) methods are designed to choose the most informative samples. A method for reducing information redundancy amongst the selected samples and a method with varying sample preference are considered. Four classifiers (k-nearest neighbor (KNN), support vector machine (SVM), linear discriminant analysis (LDA) and decision tree (DT)) are coupled with ACS methods to explore the interaction and possible preference between classifiers and ACS methods. Three kinds of measures are adopted to highlight different aspect of classification performance of these AL-boosted classifiers. Overall, AL proves its strong potential with 4% to 78% reduction on training samples in different settings. The SVM classifier shows to be the best one for using in the AL frame, with perfect performance evolving curves in different kinds of measures. The exploration and exploitation criterion can further improve the performance of the AL-boosted SVM classifier but not of the other classifiers. Full article
(This article belongs to the Special Issue Ocean Remote Sensing with Synthetic Aperture Radar)
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Open AccessArticle Detection of Bivalve Beds on Exposed Intertidal Flats Using Polarimetric SAR Indicators
Remote Sens. 2017, 9(10), 1047; doi:10.3390/rs9101047
Received: 24 June 2017 / Revised: 26 September 2017 / Accepted: 10 October 2017 / Published: 13 October 2017
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Abstract
We propose new indicators for bivalve (oyster and mussel) beds on exposed intertidal flats, derived from dual-copolarization (HH + VV) TerraSAR-X, Radarsat-2, and ALOS-2 images of the German North Sea coast. Our analyses are based upon the Kennaugh element framework, and we show
[...] Read more.
We propose new indicators for bivalve (oyster and mussel) beds on exposed intertidal flats, derived from dual-copolarization (HH + VV) TerraSAR-X, Radarsat-2, and ALOS-2 images of the German North Sea coast. Our analyses are based upon the Kennaugh element framework, and we show that different targets on exposed intertidal flats exhibit different radar backscattering characteristics, which manifest in different magnitudes of the Kennaugh elements. Namely, the inter-channel correlation’s real (K3) and imaginary (K7) part can be used to distinguish bivalve beds from surrounding sandy sediments, and together with the polarimetric coefficient (i.e., the normalized differential polarization ratio, K0/K4) they can be used as indicators for bivalve beds using multi-frequency dual-copolarization SAR data. Our results show that continuous bivalve bed monitoring is possible using dual-copolarimetric SAR acquisitions at all radar wavelengths. Full article
(This article belongs to the Special Issue Ocean Remote Sensing with Synthetic Aperture Radar)
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Open AccessArticle Assimilation of Typhoon Wind Field Retrieved from Scatterometer and SAR Based on the Huber Norm Quality Control
Remote Sens. 2017, 9(10), 987; doi:10.3390/rs9100987
Received: 17 June 2017 / Revised: 7 September 2017 / Accepted: 20 September 2017 / Published: 23 September 2017
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Abstract
Observations of sea surface wind field are critical for typhoon prediction. The scatterometer observation is one of the most important sources of sea surface winds, which provides both wind speed and wind direction information. However, the spatial resolution of scatterometer wind is low.
[...] Read more.
Observations of sea surface wind field are critical for typhoon prediction. The scatterometer observation is one of the most important sources of sea surface winds, which provides both wind speed and wind direction information. However, the spatial resolution of scatterometer wind is low. Synthetic Aperture Radar (SAR) can provide a more detailed wind structure of the tropical cyclone. In addition, the cross-polarization observation of SAR can provide more detailed information of high speed wind (>25 m·s 1 ) than the scatterometer. Nevertheless, due to the narrow swath of SAR, the number of retrieved sea surface wind data used in the data assimilation is limited, and another limitation of SAR wind observation is that it does not provide true wind direction information. In this paper, the joint assimilation of the Advanced Scatterometer (ASCAT) wind and Sentinel-1 SAR wind was investigated. Another limitation in the current operational typhoon prediction is the inefficient quality control (QC) method used in the data assimilation since a large number of high speed wind observations was rejected by the traditional Gaussian distribution QC. We introduce the Huber norm distribution quality control (QC) into the data assimilation successfully. A numerical simulation experiment of typhoon by Lionrock (2016) is conducted to test the proposed method. The experimental results showed that the new quality control scheme not only greatly increases the availability of wind data in the area of the typhoon center, but also improves the typhoon track prediction, as well as the intensity prediction. The joint assimilation of scatterometer and SAR winds does have a positive impact on the typhoon prediction. Full article
(This article belongs to the Special Issue Ocean Remote Sensing with Synthetic Aperture Radar)
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Open AccessArticle Performance Analysis of Ocean Surface Topography Altimetry by Ku-Band Near-Nadir Interferometric SAR
Remote Sens. 2017, 9(9), 933; doi:10.3390/rs9090933
Received: 14 August 2017 / Revised: 4 September 2017 / Accepted: 6 September 2017 / Published: 9 September 2017
PDF Full-text (5437 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Interferometric imaging radar altimeter (InIRA) is the first spaceborne Ku-band interferometric synthetic aperture radar (InSAR) which is specially designed for ocean surface topography altimetry. It is on the Tiangong II space laboratory, which was launched on 15 September 2016. Different from any other
[...] Read more.
Interferometric imaging radar altimeter (InIRA) is the first spaceborne Ku-band interferometric synthetic aperture radar (InSAR) which is specially designed for ocean surface topography altimetry. It is on the Tiangong II space laboratory, which was launched on 15 September 2016. Different from any other spaceborne synthetic aperture radar (SAR), InIRA chooses a near-nadir incidence of 1°~8° in order to increase the altimetric precision and swath width. Limited by the size of the Tiangong II capsule, the baseline length of InIRA is only 2.3 m. However, benefitting from the low orbit, the signal-to-noise ratio of InIRA-acquired data is above 10 dB in most of the swath, which, to a certain extent, compensates for the short baseline deficiency. The altimetric precision is simulated based on the system parameters of InIRA. Results show that it is better than 7 cm on a 5-km grid and improves to 3 cm on a 10-km grid when the incidence is below 7.4°. The interferometric data of InIRA are processed to estimate the altimetric precision after a series of procedures (including image coregistration, flat-earth-phase removal, system parameters calibration and phase noise suppression). Results show that the estimated altimetric precision is close to but lower than the simulated precision among most of the swath. The intensity boundary phenomenon is first found between the near range and far range of the SAR images of InIRA. It can be explained by the modulation of ocean internal waves or oil slick, which smooths ocean surface roughness and causes the modulated area to appear either brighter or darker than its surroundings. This intensity boundary phenomenon indicates that the available swath of high altimetric precision will be narrower than expected. Full article
(This article belongs to the Special Issue Ocean Remote Sensing with Synthetic Aperture Radar)
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Open AccessArticle Satellite Survey of Internal Waves in the Black and Caspian Seas
Remote Sens. 2017, 9(9), 892; doi:10.3390/rs9090892
Received: 30 June 2017 / Revised: 24 August 2017 / Accepted: 25 August 2017 / Published: 28 August 2017
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Abstract
The paper discusses the results of a study of short-period internal waves (IWs) in the Black and Caspian Seas from their surface manifestations in satellite imagery. Since tides are negligible in these seas, they can be considered non-tidal. Consequently, the main generation mechanism
[...] Read more.
The paper discusses the results of a study of short-period internal waves (IWs) in the Black and Caspian Seas from their surface manifestations in satellite imagery. Since tides are negligible in these seas, they can be considered non-tidal. Consequently, the main generation mechanism of IWs in the ocean—interaction of barotropic tides with bathymetry—is irrelevant. A statistically significant survey of IW occurrences in various regions of the two seas is presented. Detailed maps of spatial distribution of surface manifestations of internal waves (SMIWs) are compiled. Factors facilitating generation of IWs are determined, and a comprehensive discussion of IW generation mechanisms is presented. In the eastern and western coastal zones of the Black Sea, where large rivers disembogue, intrusions of fresh water create hydrological fronts that are able to generate IWs. At the continental shelf edge, on the west and northwest of the Black Sea and near the Crimean Peninsula, IWs are generated primarily due to relaxation of coastal upwelling and inertial oscillations associated with hydrological fronts. In addition, IWs can be formed at sea fronts associated with the passage of cold eddies. In the Caspian Sea, seiches are the main source of the observed IWs. Full article
(This article belongs to the Special Issue Ocean Remote Sensing with Synthetic Aperture Radar)
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Open AccessArticle Azimuth Ambiguities Removal in Littoral Zones Based on Multi-Temporal SAR Images
Remote Sens. 2017, 9(8), 866; doi:10.3390/rs9080866
Received: 31 May 2017 / Revised: 16 August 2017 / Accepted: 17 August 2017 / Published: 22 August 2017
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Abstract
Synthetic aperture radar (SAR) is one of the most important techniques for ocean monitoring. Azimuth ambiguities are a real problem in SAR images today, which can cause performance degradation in SAR ocean applications. In particular, littoral zones can be strongly affected by land-based
[...] Read more.
Synthetic aperture radar (SAR) is one of the most important techniques for ocean monitoring. Azimuth ambiguities are a real problem in SAR images today, which can cause performance degradation in SAR ocean applications. In particular, littoral zones can be strongly affected by land-based sources, whereas they are usually regions of interest (ROI). Given the presence of complexity and diversity in littoral zones, azimuth ambiguities removal is a tough problem. As SAR sensors can have a repeat cycle, multi-temporal SAR images provide new insight into this problem. A method for azimuth ambiguities removal in littoral zones based on multi-temporal SAR images is proposed in this paper. The proposed processing chain includes co-registration, local correlation, binarization, masking, and restoration steps. It is designed to remove azimuth ambiguities caused by fixed land-based sources. The idea underlying the proposed method is that sea surface is dynamic, whereas azimuth ambiguities caused by land-based sources are constant. Thus, the temporal consistence of azimuth ambiguities is higher than sea clutter. It opens up the possibilities to use multi-temporal SAR data to remove azimuth ambiguities. The design of the method and the experimental procedure are based on images from the Sentinel data hub of Europe Space Agency (ESA). Both Interferometric Wide Swath (IW) and Stripmap (SM) mode images are taken into account to validate the proposed method. This paper also presents two RGB composition methods for better azimuth ambiguities visualization. Experimental results show that the proposed method can remove azimuth ambiguities in littoral zones effectively. Full article
(This article belongs to the Special Issue Ocean Remote Sensing with Synthetic Aperture Radar)
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Open AccessArticle Contextual Region-Based Convolutional Neural Network with Multilayer Fusion for SAR Ship Detection
Remote Sens. 2017, 9(8), 860; doi:10.3390/rs9080860
Received: 21 July 2017 / Revised: 9 August 2017 / Accepted: 9 August 2017 / Published: 20 August 2017
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Abstract
Synthetic aperture radar (SAR) ship detection has been playing an increasingly essential role in marine monitoring in recent years. The lack of detailed information about ships in wide swath SAR imagery poses difficulty for traditional methods in exploring effective features for ship discrimination.
[...] Read more.
Synthetic aperture radar (SAR) ship detection has been playing an increasingly essential role in marine monitoring in recent years. The lack of detailed information about ships in wide swath SAR imagery poses difficulty for traditional methods in exploring effective features for ship discrimination. Being capable of feature representation, deep neural networks have achieved dramatic progress in object detection recently. However, most of them suffer from the missing detection of small-sized targets, which means that few of them are able to be employed directly in SAR ship detection tasks. This paper discloses an elaborately designed deep hierarchical network, namely a contextual region-based convolutional neural network with multilayer fusion, for SAR ship detection, which is composed of a region proposal network (RPN) with high network resolution and an object detection network with contextual features. Instead of using low-resolution feature maps from a single layer for proposal generation in a RPN, the proposed method employs an intermediate layer combined with a downscaled shallow layer and an up-sampled deep layer to produce region proposals. In the object detection network, the region proposals are projected onto multiple layers with region of interest (ROI) pooling to extract the corresponding ROI features and contextual features around the ROI. After normalization and rescaling, they are subsequently concatenated into an integrated feature vector for final outputs. The proposed framework fuses the deep semantic and shallow high-resolution features, improving the detection performance for small-sized ships. The additional contextual features provide complementary information for classification and help to rule out false alarms. Experiments based on the Sentinel-1 dataset, which contains twenty-seven SAR images with 7986 labeled ships, verify that the proposed method achieves an excellent performance in SAR ship detection. Full article
(This article belongs to the Special Issue Ocean Remote Sensing with Synthetic Aperture Radar)
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Open AccessArticle Technical Evaluation of Sentinel-1 IW Mode Cross-Pol Radar Backscattering from the Ocean Surface in Moderate Wind Condition
Remote Sens. 2017, 9(8), 854; doi:10.3390/rs9080854
Received: 30 June 2017 / Revised: 5 August 2017 / Accepted: 16 August 2017 / Published: 17 August 2017
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Abstract
The Sentinel-1 synthetic aperture radar (SAR) allows sufficient resources for cross-pol wind speed retrievals over the ocean. In this paper, we present technical evaluation on wind retrieval from both Sentinel-1A and Sentinel-1B IW cross-pol images. Algorithms are based on the existing theoretical and
[...] Read more.
The Sentinel-1 synthetic aperture radar (SAR) allows sufficient resources for cross-pol wind speed retrievals over the ocean. In this paper, we present technical evaluation on wind retrieval from both Sentinel-1A and Sentinel-1B IW cross-pol images. Algorithms are based on the existing theoretical and empirical ones derived from the RADARSAT-2 cross-pol data. First, to better understand the Sentinel-1 observed normalized radar cross section (NRCS) values under various environmental conditions, we constructed a dataset that integrates SAR images with wind field information from scatterometer measurements. There are 11,883 matchup data in the experimental dataset. We then calculated the systemic noise floor of Sentinel-1 IW mode, and presented its unique noise characteristics among different sub-bands. Based on the calculated NESZ measurements, the noise is removed for all matchup data. Empirical relationships among the noise free NRCS σ VH 0 , wind speed, wind direction, and radar incidence angle are analyzed for each sub-band, and a piecewise model is proposed. We showed that a larger correlation coefficient, r, is achieved by including both wind direction and incidence terms in the model. Validation against scatterometer measurements showed the suitability of the proposed model. Full article
(This article belongs to the Special Issue Ocean Remote Sensing with Synthetic Aperture Radar)
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Open AccessArticle Assimilation of Sentinel-1 Derived Sea Surface Winds for Typhoon Forecasting
Remote Sens. 2017, 9(8), 845; doi:10.3390/rs9080845
Received: 16 June 2017 / Revised: 9 August 2017 / Accepted: 10 August 2017 / Published: 14 August 2017
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Abstract
High-resolution synthetic aperture radar (SAR) wind observations provide fine structural information for tropical cycles and could be assimilated into numerical weather prediction (NWP) models. However, in the conventional method assimilating the u and v components for SAR wind observations (SAR_uv), the wind direction
[...] Read more.
High-resolution synthetic aperture radar (SAR) wind observations provide fine structural information for tropical cycles and could be assimilated into numerical weather prediction (NWP) models. However, in the conventional method assimilating the u and v components for SAR wind observations (SAR_uv), the wind direction is not a state vector and its observational error is not considered during the assimilation calculation. In this paper, an improved method for wind observation directly assimilates the SAR wind observations in the form of speed and direction (SAR_sd). This method was implemented to assimilate the sea surface wind retrieved from Sentinel-1 synthetic aperture radar (SAR) in the basic three-dimensional variational system for the Weather Research and Forecasting Model (WRF 3DVAR). Furthermore, a new quality control scheme for wind observations is also presented. Typhoon Lionrock in August 2016 is chosen as a case study to investigate and compare both assimilation methods. The experimental results show that the SAR wind observations can increase the number of the effective observations in the area of a typhoon and have a positive impact on the assimilation analysis. The numerical forecast results for this case show better results for the SAR_sd method than for the SAR_uv method. The SAR_sd method looks very promising for winds assimilation under typhoon conditions, but more cases need to be considered to draw final conclusions. Full article
(This article belongs to the Special Issue Ocean Remote Sensing with Synthetic Aperture Radar)
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Open AccessArticle Refocusing of Moving Targets in SAR Images via Parametric Sparse Representation
Remote Sens. 2017, 9(8), 795; doi:10.3390/rs9080795
Received: 12 June 2017 / Revised: 28 July 2017 / Accepted: 31 July 2017 / Published: 2 August 2017
Cited by 1 | PDF Full-text (6767 KB) | HTML Full-text | XML Full-text
Abstract
In this paper, a parametric sparse representation (PSR) method is proposed for refocusing of moving targets in synthetic aperture radar (SAR) images. In regular SAR images, moving targets are defocused due to unknown motion parameters. Refocusing of moving targets requires accurate phase compensation
[...] Read more.
In this paper, a parametric sparse representation (PSR) method is proposed for refocusing of moving targets in synthetic aperture radar (SAR) images. In regular SAR images, moving targets are defocused due to unknown motion parameters. Refocusing of moving targets requires accurate phase compensation of echo data. In the proposed method, the region of interest (ROI) data containing the moving targets are extracted from the complex SAR image and represented in a sparse fashion through a parametric transform, which is related to the phase compensation parameter. By updating the reflectivities of moving target scatterers and the parametric transform in an iterative fashion, the phase compensation parameter can be accurately estimated and the SAR images of moving targets can be refocused well. The proposed method directly operates on small-size defocused ROI data, which helps to reduce the computational burden and suppress the clutter. Compared to other existing ROI-based methods, the proposed method can suppress asymmetric side-lobes and improve the image quality. Both simulated data and real SAR data collected by GF-3 satellite are used to validate the effectiveness of the proposed method. Full article
(This article belongs to the Special Issue Ocean Remote Sensing with Synthetic Aperture Radar)
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Open AccessFeature PaperArticle An Empirical Algorithm for Wave Retrieval from Co-Polarization X-Band SAR Imagery
Remote Sens. 2017, 9(7), 711; doi:10.3390/rs9070711
Received: 17 April 2017 / Revised: 28 June 2017 / Accepted: 6 July 2017 / Published: 11 July 2017
Cited by 1 | PDF Full-text (5775 KB) | HTML Full-text | XML Full-text
Abstract
In this study, we proposed an empirical algorithm for significant wave height (SWH) retrieval from TerraSAR-X/TanDEM (TS-X/TD-X) X-band synthetic aperture radar (SAR) co-polarization (vertical-vertical (VV) and horizontal-horizontal (HH)) images. As the existing empirical algorithm at X-band, i.e., XWAVE, is applied for wave retrieval
[...] Read more.
In this study, we proposed an empirical algorithm for significant wave height (SWH) retrieval from TerraSAR-X/TanDEM (TS-X/TD-X) X-band synthetic aperture radar (SAR) co-polarization (vertical-vertical (VV) and horizontal-horizontal (HH)) images. As the existing empirical algorithm at X-band, i.e., XWAVE, is applied for wave retrieval from HH-polarization TS-X/TD-X image, polarization ratio (PR) has to be used for inverting wind speed, which is treated as an input in XWAVE. Wind speed encounters saturation in tropical cyclone. In our work, wind speed is replaced by normalized radar cross section (NRCS) to avoiding using SAR-derived wind speed, which does not work in high winds, and the empirical algorithm can be conveniently implemented without converting NRCS in HH-polarization to NRCS in VV-polarization by using X-band PR. A total of 120 TS-X/TD-X images, 60 in VV-polarization and 60 in HH-polarization, with homogenous wave patterns, and the coincide significant wave height data from European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis field at a 0.125° grid were collected as a dataset for tuning the algorithm. The range of SWH is from 0 to 7 m. We then applied the algorithm to 24 VV and 21 HH additional SAR images to extract SWH at locations of 30 National Oceanic and Atmospheric Administration (NOAA) National Data Buoy Center (NDBC) buoys. It is found that the algorithm performs well with a SWH stander deviation (STD) of about 0.5 m for both VV and HH polarization TS-X/TD-X images. For large wave validation (SWH 6–7 m), we applied the empirical algorithm to a tropical cyclone Sandy TD-X image acquired in 2012, and obtained good result with a SWH STD of 0.3 m. We concluded that the proposed empirical algorithm works for wave retrieval from TS-X/TD-X image in co-polarization without external sea surface wind information. Full article
(This article belongs to the Special Issue Ocean Remote Sensing with Synthetic Aperture Radar)
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Open AccessArticle An Improved Local Gradient Method for Sea Surface Wind Direction Retrieval from SAR Imagery
Remote Sens. 2017, 9(7), 671; doi:10.3390/rs9070671
Received: 21 April 2017 / Revised: 23 June 2017 / Accepted: 26 June 2017 / Published: 30 June 2017
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Abstract
Sea surface wind affects the fluxes of energy, mass and momentum between the atmosphere and ocean, and therefore regional and global weather and climate. With various satellite microwave sensors, sea surface wind can be measured with large spatial coverage in almost all-weather conditions,
[...] Read more.
Sea surface wind affects the fluxes of energy, mass and momentum between the atmosphere and ocean, and therefore regional and global weather and climate. With various satellite microwave sensors, sea surface wind can be measured with large spatial coverage in almost all-weather conditions, day or night. Like any other remote sensing measurements, sea surface wind measurement is also indirect. Therefore, it is important to develop appropriate wind speed and direction retrieval models for different types of microwave instruments. In this paper, a new sea surface wind direction retrieval method from synthetic aperture radar (SAR) imagery is developed. In the method, local gradients are computed in frequency domain by combining the operation of smoothing and computing local gradients in one step to simplify the process and avoid the difference approximation. This improved local gradients (ILG) method is compared with the traditional two-dimensional fast Fourier transform (2D FFT) method and local gradients (LG) method, using interpolating wind directions from the European Centre for Medium-Range Weather Forecast (ECMWF) reanalysis data and the Cross-Calibrated Multi-Platform (CCMP) wind vector product. The sensitivities to the salt-and-pepper noise, the additive noise and the multiplicative noise are analyzed. The ILG method shows a better performance of retrieval wind directions than the other two methods. Full article
(This article belongs to the Special Issue Ocean Remote Sensing with Synthetic Aperture Radar)
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Open AccessArticle Underwater Topography Detection in Coastal Areas Using Fully Polarimetric SAR Data
Remote Sens. 2017, 9(6), 560; doi:10.3390/rs9060560
Received: 27 February 2017 / Revised: 22 May 2017 / Accepted: 31 May 2017 / Published: 4 June 2017
PDF Full-text (3259 KB) | HTML Full-text | XML Full-text
Abstract
Fully polarimetric synthetic aperture radar (SAR) can provide detailed information on scattering mechanisms that could enable the target or structure to be identified. This paper presents a method to detect underwater topography in coastal areas using high resolution fully polarimetric SAR data, while
[...] Read more.
Fully polarimetric synthetic aperture radar (SAR) can provide detailed information on scattering mechanisms that could enable the target or structure to be identified. This paper presents a method to detect underwater topography in coastal areas using high resolution fully polarimetric SAR data, while less prior information is required. The method is based on the shoaling and refraction of long surface gravity waves as they propagate shoreward. First, the surface scattering component is obtained by polarization decomposition. Then, wave fields are retrieved from the two-dimensional (2D) spectra by the Fast Fourier Transformation (FFT). Finally, shallow water depths are estimated from the dispersion relation. Applicability and effectiveness of the proposed methodology are tested by using C-band fine quad-polarization mode RADARSAT-2 SAR data over the near-shore area of the Hainan province, China. By comparing with the values from an official electronic navigational chart (ENC), the estimated water depths are in good agreement with them. The average relative error of the detected results from the scattering mechanisms based method and single polarization SAR data are 9.73% and 11.53% respectively. The validation results indicate that the scattering mechanisms based methodology is more effective than only using the single polarization SAR data for underwater topography detection, and will inspire further research on underwater topography detection with fully polarimetric SAR data. Full article
(This article belongs to the Special Issue Ocean Remote Sensing with Synthetic Aperture Radar)
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Open AccessArticle Modulation Model of High Frequency Band Radar Backscatter by the Internal Wave Based on the Third-Order Statistics
Remote Sens. 2017, 9(5), 501; doi:10.3390/rs9050501
Received: 31 March 2017 / Revised: 12 May 2017 / Accepted: 17 May 2017 / Published: 19 May 2017
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Abstract
Modulation model of radar backscatters is an important topic in the remote sensing of oceanic internal wave by synthetic aperture radar (SAR). Previous studies related with the modulation models were analyzed mainly based on the hypothesis that ocean surface waves are Gaussian distributed.
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Modulation model of radar backscatters is an important topic in the remote sensing of oceanic internal wave by synthetic aperture radar (SAR). Previous studies related with the modulation models were analyzed mainly based on the hypothesis that ocean surface waves are Gaussian distributed. However, this is not always true for the complicated ocean environment. Research has showed that the measurements are usually larger than the values predicted by modulation models for the high frequency radars (X-band and above). In this paper, a new modulation model was proposed which takes the third-order statistics of the ocean surface into account. It takes the situation into consideration that the surface waves are Non-Gaussian distributed under some conditions. The model can explain the discrepancy between the measurements and the values calculated by the traditional models in theory. Furthermore, it can accurately predict the modulation for the higher frequency band. The model was verified by the experimental measurements recorded in a wind wave tank. Further discussion was made about applicability of this model that it performs better in the prediction of radar backscatter modulation compared with the traditional modulation model for the high frequency band radar or under lager wind speeds. Full article
(This article belongs to the Special Issue Ocean Remote Sensing with Synthetic Aperture Radar)
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Open AccessArticle Ku-Band Sea Surface Radar Backscatter at Low Incidence Angles under Extreme Wind Conditions
Remote Sens. 2017, 9(5), 474; doi:10.3390/rs9050474
Received: 14 March 2017 / Revised: 18 April 2017 / Accepted: 9 May 2017 / Published: 12 May 2017
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Abstract
This paper reports Ku-band normalized radar cross section (NRCS) at low incidence angles ranging from 0° to 18° and in the wind speed range from 6 to 70 m/s. The precipitation radar onboard the tropical rainfall measuring mission and Jason-1 and 2 have
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This paper reports Ku-band normalized radar cross section (NRCS) at low incidence angles ranging from 0° to 18° and in the wind speed range from 6 to 70 m/s. The precipitation radar onboard the tropical rainfall measuring mission and Jason-1 and 2 have provided 152 hurricanes observations between 2008 and 2013 that were collocated with stepped-frequency microwave radiometer measurements. It is found that the NRCS decreases with increasing incidence angle. The decrease is more dramatic in the 40–70 m/s range of wind speeds than in the 6–20 m/s range, indicating that the NRCS is very sensitive to low incidence angles under extreme wind conditions and insensitive to the extreme wind speed. Consequently, the sea surface appears relatively “smooth” to Ku-band electromagnetic microwaves. This phenomenon validates the observed drag coefficient reduction under extreme wind conditions, from a remote sensing viewpoint. Using the NRCS dependence on incidence angle under extreme wind conditions, we also present an empirical linear relationship between NRCS and incidence angles, which may assist future-satellites missions operating at small incidence angles to measure sea surface wind and wave field. Full article
(This article belongs to the Special Issue Ocean Remote Sensing with Synthetic Aperture Radar)
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Open AccessArticle An ML-Based Radial Velocity Estimation Algorithm for Moving Targets in Spaceborne High-Resolution and Wide-Swath SAR Systems
Remote Sens. 2017, 9(5), 404; doi:10.3390/rs9050404
Received: 28 February 2017 / Revised: 20 April 2017 / Accepted: 21 April 2017 / Published: 26 April 2017
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Abstract
Multichannel synthetic aperture radar (SAR) is a significant breakthrough to the inherent limitation between high-resolution and wide-swath (HRWS) compared with conventional SAR. Moving target indication (MTI) is an important application of spaceborne HRWS SAR systems. In contrast to previous studies of SAR MTI,
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Multichannel synthetic aperture radar (SAR) is a significant breakthrough to the inherent limitation between high-resolution and wide-swath (HRWS) compared with conventional SAR. Moving target indication (MTI) is an important application of spaceborne HRWS SAR systems. In contrast to previous studies of SAR MTI, the HRWS SAR mainly faces the problem of under-sampled data of each channel, causing single-channel imaging and processing to be infeasible. In this study, the estimation of velocity is equivalent to the estimation of the cone angle according to their relationship. The maximum likelihood (ML) based algorithm is proposed to estimate the radial velocity in the existence of Doppler ambiguities. After that, the signal reconstruction and compensation for the phase offset caused by radial velocity are processed for a moving target. Finally, the traditional imaging algorithm is applied to obtain a focused moving target image. Experiments are conducted to evaluate the accuracy and effectiveness of the estimator under different signal-to-noise ratios (SNR). Furthermore, the performance is analyzed with respect to the motion ship that experiences interference due to different distributions of sea clutter. The results verify that the proposed algorithm is accurate and efficient with low computational complexity. This paper aims at providing a solution to the velocity estimation problem in the future HRWS SAR systems with multiple receive channels. Full article
(This article belongs to the Special Issue Ocean Remote Sensing with Synthetic Aperture Radar)
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Open AccessArticle Directional Spreading Function of the Gravity-Capillary Wave Spectrum Derived from Radar Observations
Remote Sens. 2017, 9(4), 361; doi:10.3390/rs9040361
Received: 3 December 2016 / Revised: 19 March 2017 / Accepted: 1 April 2017 / Published: 12 April 2017
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Abstract
Directional spreading function of the gravity-capillary wave spectrum can provide the high-wavenumber wave energy distribution among different directions on the sea surface. The existing directional spreading functions have been mainly developed for the low-wavenumber gravity wave with buoy data. In this paper, we
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Directional spreading function of the gravity-capillary wave spectrum can provide the high-wavenumber wave energy distribution among different directions on the sea surface. The existing directional spreading functions have been mainly developed for the low-wavenumber gravity wave with buoy data. In this paper, we use radar observations to derive the directional spreading function of the gravity-capillary wave spectrum, which is expressed as the second-order Fourier series expansion. So far the standard form of the second-order harmonic coefficient has not been proposed to correctly unify the gravity and gravity-capillary wave. Our strategy is to introduce a correcting term to replace the inaccurate gravity-capillary spectral component in Elfouhaily’s directional spreading function. The second-order harmonic coefficient at L, C and Ku band calculated by the radar observation is used to fit the correcting term to obtain one at the full gravity-capillary wave region. According to our proposed the directional spreading function, there is a spectral region between the gravity and gravity-capillary range where it signifies the negative upwind–crosswind asymmetry at low and moderate speed range. And this is not reflected by the previous models, but has been confirmed by radar observations. The Root Mean Square Difference (RMSD) of the proposed second-order harmonic coefficient versus the radar-observed one at L, C band Ku band is 0.0438, 0.0263 and 0.0382, respectively. The overall bias and RMSD are −0.0029 and 0.0433 for the whole second-order harmonic coefficient range, respectively. The result verifies the accuracy of the proposed directional spreading function at L, C band Ku band. Full article
(This article belongs to the Special Issue Ocean Remote Sensing with Synthetic Aperture Radar)
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Open AccessArticle Doppler Spectrum-Based NRCS Estimation Method for Low-Scattering Areas in Ocean SAR Images
Remote Sens. 2017, 9(3), 219; doi:10.3390/rs9030219
Received: 6 December 2016 / Revised: 24 February 2017 / Accepted: 25 February 2017 / Published: 28 February 2017
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Abstract
The image intensities of low-backscattering areas in synthetic aperture radar (SAR) images are often seriously contaminated by the system noise floor and azimuthal ambiguity signal from adjacent high-backscattering areas. Hence, the image intensity of low-backscattering areas does not correctly reflect the backscattering intensity,
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The image intensities of low-backscattering areas in synthetic aperture radar (SAR) images are often seriously contaminated by the system noise floor and azimuthal ambiguity signal from adjacent high-backscattering areas. Hence, the image intensity of low-backscattering areas does not correctly reflect the backscattering intensity, which causes confusion in subsequent image processing or interpretation. In this paper, a method is proposed to estimate the normalized radar cross-section (NRCS) of low-backscattering area by utilizing the differences between noise, azimuthal ambiguity, and signal in the Doppler frequency domain of single-look SAR images; the aim is to eliminate the effect of system noise and azimuthal ambiguity. Analysis shows that, for a spaceborne SAR with a noise equivalent sigma zero (NESZ) of −25 dB and a single-look pixel of 8 m × 5 m, the NRCS-estimation precision of this method can reach −38 dB at a resolution of 96 m × 100 m. Three examples are given to validate the advantages of this method in estimating the low NRCS and the filtering of the azimuthal ambiguity. Full article
(This article belongs to the Special Issue Ocean Remote Sensing with Synthetic Aperture Radar)
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Open AccessArticle An Improved Shape Contexts Based Ship Classification in SAR Images
Remote Sens. 2017, 9(2), 145; doi:10.3390/rs9020145
Received: 8 December 2016 / Accepted: 4 February 2017 / Published: 10 February 2017
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Abstract
In synthetic aperture radar (SAR) imagery, relating to maritime surveillance studies, the ship has always been the main focus of study. In this letter, a method of ship classification in SAR images is proposed to enhance classification accuracy. In the proposed method, to
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In synthetic aperture radar (SAR) imagery, relating to maritime surveillance studies, the ship has always been the main focus of study. In this letter, a method of ship classification in SAR images is proposed to enhance classification accuracy. In the proposed method, to fully exploit the distinguishing characters of the ship targets, both topology and intensity of the scattering points of the ship are considered. The results of testing the proposed method on a data set of three types of ships, collected via a space-borne SAR sensor designed by the Institute of Electronics, Chinese Academy of Sciences (IECAS), establish that the proposed method is superior to several existing methods, including the original shape contexts method, traditional invariant moments and the recent approach. Full article
(This article belongs to the Special Issue Ocean Remote Sensing with Synthetic Aperture Radar)
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Open AccessLetter GF-3 SAR Ocean Wind Retrieval: The First View and Preliminary Assessment
Remote Sens. 2017, 9(7), 694; doi:10.3390/rs9070694
Received: 4 June 2017 / Revised: 22 June 2017 / Accepted: 4 July 2017 / Published: 5 July 2017
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Abstract
Gaofen-3 (GF-3) is the first Chinese civil C-band synthetic aperture radar (SAR) launched on 10 August 2016 by the China Academy of Space Technology (CAST), which operates in 12 imaging modes with a fine spatial resolution up to 1 m. As one of
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Gaofen-3 (GF-3) is the first Chinese civil C-band synthetic aperture radar (SAR) launched on 10 August 2016 by the China Academy of Space Technology (CAST), which operates in 12 imaging modes with a fine spatial resolution up to 1 m. As one of the primary users, the State Oceanic Administration (SOA) operationally processes GF-3 SAR Level-1 products into ocean surface wind vector and plans to officially release the near real-time SAR wind products in the near future. In this paper, the methodology of wind retrieval at C-band SAR is introduced and the first results of GF-3 SAR-derived winds are presented. In particular, the case of the coastal katabatic wind off the west coast of the U.S. captured by GF-3 is discussed. The preliminary accuracy assessment of wind speed and direction retrievals from GF-3 SAR is carried out against in situ measurements from National Data Buoy Center (NDBC) buoy measurements of National Oceanic and Atmospheric Administration (NOAA). Only the buoys located inside the GF-3 SAR wind cell (1 km) were considered as co-located in space, while the time interval between observations of SAR and buoy was limited to less the 30 min. These criteria yielded 56 co-locations during the period from January to April 2017, showing the Root Mean Square Error (RMSE) of 2.46 m/s and 22.22° for wind speed and direction, respectively. Different performances due to geophysical model function (GMF) and Polarization Ratio (PR) are discussed. The preliminary results indicate that GF-3 wind retrievals are encouraging for operational implementation. Full article
(This article belongs to the Special Issue Ocean Remote Sensing with Synthetic Aperture Radar)
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