Operational Ship Monitoring System Based on Synthetic Aperture Radar Processing

Margarit, G.; Milanés, José A. Barba; Tabasco, Antonio

doi:10.3390/rs1030375

Cited by 43 publications

(28 citation statements)

References 5 publications

(6 reference statements)

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“…Such techniques mainly involve applications related to ship detection, land mask estimation in those situations where geographic maps cannot be used (natural disasters, marshlands and wetlands) and oil-spill detection, among others. Furthermore, maritime applications have become one of the SAR major fields of study in recent years [19][20][21][22][23][24]. All in all, the absence of studies of this particular issue along with the difference it can make in automatic processing, make this topic an interesting one to cover.…”

Section: Introductionmentioning

confidence: 99%

Statistical Analysis of SAR Sea Clutter for Classification Purposes

et al. 2014

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Statistical analysis of radar clutter has always been one of the topics, where more effort has been put in the last few decades. These studies were usually focused on finding the statistical models that better fitted the clutter distribution; however, the goal of this work is not the modeling of the clutter, but the study of the suitability of the statistical parameters to carry out a sea state classification. In order to achieve this objective and provide some relevance to this study, an important set of maritime and coastal Synthetic Aperture Radar data is considered. Due to the nature of the acquisition of data by SAR sensors, speckle noise is inherent to these data, and a specific study of how this noise affects the clutter distribution is also performed in this work. In pursuit of a sense of wholeness, a thorough study of the most suitable statistical parameters, as well as the most adequate classifier is carried out, achieving excellent results in terms of classification success rates. These concluding results confirm that a sea state classification is not only viable, but also successful using statistical parameters different from those of the best modeling distribution and applying a speckle filter, which allows a better characterization of the parameters used to distinguish between different sea states.

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Section: Introductionmentioning

confidence: 99%

Statistical Analysis of SAR Sea Clutter for Classification Purposes

et al. 2014

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“…In addition, the detection of small vessel may be improved by using corner reflectors, as proposed in (Stastny et al, 2014). An operational ship monitoring system based on SAR processing is presented in (Margarit et al, 2009). …”

Section: Sar For Vessel Surveillancementioning

confidence: 99%

Sentinel-1/2 Data for Ship Traffic Monitoring on the Danube River

Negula

Poenaru

Olteanu

et al. 2016

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:After a long period of drought, the water level of the Danube River has significantly dropped especially on the Romanian sector, in July-August 2015. Danube reached the lowest water level recorded in the last 12 years, causing the blockage of the ships in the sector located close to Zimnicea Harbour. The rising sand banks in the navigable channel congested the commercial traffic for a few days with more than 100 ships involved. The monitoring of the decreasing water level and the traffic jam was performed based on Sentinel-1 and Sentinel-2 free data provided by the European Space Agency and the European Commission within the Copernicus Programme. Specific processing methods (calibration, speckle filtering, geocoding, change detection, image classification, principal component analysis, etc.) were applied in order to generate useful products that the responsible authorities could benefit from. The Sentinel data yielded good results for water mask extraction and ships detection. The analysis continued after the closure of the crisis situation when the water reached the nominal level again. The results indicate that Sentinel data can be successfully used for ship traffic monitoring, building the foundation of future endeavours for a durable monitoring of the Danube River.

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“…Margarit et al presented a vessel classification algorithm based on fuzzy logic. Experimental results based on the ENVISAT-ASAR and ERS data at a resolution of approximately 30 meters showed that the preliminary percentage of positive classifications was approximately 70% [3,13]. In medium resolution SAR images (30 to 10 m), energy scattered from different parts of a vessel usually overlap in a few pixels.…”

Section: Introductionmentioning

confidence: 99%

Classification of Vessels in Single-Pol COSMO-SkyMed Images Based on Statistical and Structural Features

Wang

Jiang

et al. 2015

Remote Sensing

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Vessel monitoring is one of the most important maritime applications of Synthetic Aperture Radar (SAR) data. Because of the dihedral reflections between the vessel hull and sea surface and the trihedral reflections among superstructures, vessels usually have strong backscattering in SAR images. Furthermore, in high-resolution SAR images, detailed information on vessel structures can be observed, allowing for vessel classification in high-resolution SAR images. This paper focuses on the feature analysis of merchant vessels, including bulk carriers, container ships and oil tankers, in 3 m resolution COSMO-SkyMed stripmap HIMAGE mode images and proposes a method for vessel classification. After preprocessing, a feature vector is estimated by calculating the average value of the kernel density estimation, three structural features and the mean backscattering coefficient. Support vector machine (SVM) classifier is used for the vessel classification, and the results are compared with traditional methods, such as the K-nearest neighbor algorithm (K-NN) and minimum distance classifier (MDC). In situ investigations are conducted during the SAR data acquisition. Corresponding Automatic Identification System (AIS) reports are also obtained as ground truth to evaluate the effectiveness of the classifier. The preliminary results show that the combination of the average value of the kernel density estimation and mean backscattering coefficient has good ability for classifying the three types of vessels. When adding the three structural features, the results slightly improve. The result of the SVM classifier is better than that of K-NN and MDC. However, the SVM requires more time, when the parameters of the kernel are estimated. OPEN ACCESSRemote Sens. 2015, 7 5512

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Operational Ship Monitoring System Based on Synthetic Aperture Radar Processing

Cited by 43 publications

References 5 publications

Statistical Analysis of SAR Sea Clutter for Classification Purposes

Statistical Analysis of SAR Sea Clutter for Classification Purposes

Sentinel-1/2 Data for Ship Traffic Monitoring on the Danube River

Classification of Vessels in Single-Pol COSMO-SkyMed Images Based on Statistical and Structural Features

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