Near Real Time Applications for Maritime Situational Awareness

Schwarz, Egbert; Krause, Detmar; Berg, Matthias; Daedelow, Holger; Maass, Holger

doi:10.5194/isprsarchives-xl-7-w3-999-2015

Cited by 19 publications

(16 citation statements)

References 7 publications

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“…With the launch of C-band Sentinel-1A/B constellation, different methods to estimate meteo-marine parameters, software realizations, and infrastructure open possibilities for NRT services for oceanographic applications [16]. As shortly as 10 min after image downlink, information about wave height, wind speed, as well as ice coverage, oil spills, and ship detection can be transferred to interested institutions or weather services [13,20,[22][23][24].…”

Section: Meteo-marine Parameters In the Baltic Sea In Relation To Synmentioning

confidence: 99%

“…Empirical functions, deduced from large sets of representative data, are proven to be more suitable for the short windsea waves and noisy images. The direct estimation of wave parameters from subscene spectra allows fast, straightforward, and reliable near-real-time (NRT) processing of satellite scene while excluding only a fragment of the data [13,16].…”

Section: Meteo-marine Parameters In the Baltic Sea In Relation To Synmentioning

confidence: 99%

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Meteo-Marine Parameters from Sentinel-1 SAR Imagery: Towards Near Real-Time Services for the Baltic Sea

et al. 2018

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Abstract:A method for estimating meteo-marine parameters from satellite Synthetic Aperture Radar (SAR) data, intended for near-real-time (NRT) service over the Baltic Sea, is presented and validated. Total significant wave height data are retrieved with an empirical function CWAVE_S1-IW, which combines spectral analysis of Sentinel-1A/B Interferometric Wide swath (IW) subscenes with wind data derived with common C-Band Geophysical Model Functions (GMFs). In total, 15 Sentinel-1A/B scenes (116 acquisitions) over the Baltic Sea were processed for comparison with off-shore sea state measurements (52 collocations) and coastal wind measurements (357 colocations). Sentinel-1 wave height was spatially compared with WAM wave model results (Copernicus Marine Environment Monitoring Service (CMEMS). The comparison of SAR-derived wave heights shows good agreement with measured wave heights correlation r of 0.88 and with WAM model (r = 0.85). The wind speed estimated from SAR images yields good agreement with in situ data (r = 0.91). The study demonstrates that the wave retrievals from Sentinel-1 IW data provide valuable information for operational and statistical monitoring of wave conditions in the Baltic Sea. The data is valuable for model validation and interpretation in regions where, and during periods when, in situ measurements are missing. The Sentinel-1 A/B wave retrievals provide more detailed information about spatial variability of the wave field in the coastal zone compared to in situ measurements, altimetry wave products and model forecast. Thus, SAR data enables estimation of storm locations and areal coverage. Methods shown in the study are implemented in NRT service in German Aerospace Center's (DLR) ground station Neustrelitz.

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Section: Meteo-marine Parameters In the Baltic Sea In Relation To Synmentioning

confidence: 99%

Section: Meteo-marine Parameters In the Baltic Sea In Relation To Synmentioning

confidence: 99%

Meteo-Marine Parameters from Sentinel-1 SAR Imagery: Towards Near Real-Time Services for the Baltic Sea

et al. 2018

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“…The CWAVE_S1-IW was incorporated into the operational NRT version of the SSP initially developed for the TS-X and TanDEM-X (TD-X) satellites (Pleskachevsky, Rosenthal, and Lehner, 2016, Schwarz, et al 2015, Pleskachevsky, et al 2017. For MSA, the image can be processed into different information layers (surface wind, sea state, ship detection, etc.).…”

Section: Sea State Processor For Practical and Operational Usementioning

confidence: 99%

“…Lehner, et al 2013). The rapid development of satellite techniques, SAR sensors, SAR processors, algorithms and ground infrastructures made possible a series of oceanographic applications in NRT in recent years (Schwarz, et al 2015). Several minutes after the acquisition, the derived products can be transferred to weather services for validation of their forecasting models (Pleskachevsky, Rosenthal, and Lehner, 2016).…”

Section: Introductionmentioning

confidence: 99%

Estimation of sea state from Sentinel-1 Synthetic aperture radar imagery for maritime situation awareness

Pleskachevsky

Jacobsen

Tings

et al. 2019

International Journal of Remote Sensing

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A new empirical algorithm CWAVE_S1-IW for estimation of significant wave height H s including swell and wind sea from C-band satelliteborne Synthetic Aperture Radar (SAR) data has been developed for Sentinel-1 (S1) Interferometric Wide Swatrh Mode (IW) imagery. The algorithm was implemented into the Sea Sate Processor (SSP) for fully automatic processing for near real time (NRT) services and allow the estimation of wave fields of thousands of kilometres in the flight direction and 250 km swath from S1 IW scenes consisting of a sequence of individual images.The priority of CWAVE_S1-IW development was an automatic, fast and robust raster processing independent of wave patterns, applicable even when only clutter is visible in the SAR images. The algorithm is based on the spectral analysis of subscenes in wavenumber space. The empirical function allows direct H s estimation from image spectra without first converting them into wave spectra and uses integrated image spectra parameters as well as estimated local wind information. A texture analysis based on Grey Level Co-occurrence Matrices (GLCM) is also applied. In this way, also the parameters of short waves can be estimated, which are not visible in S1 IW images and are only represented by clutter.The algorithm was tuned worldwide using in-situ collocated measurements of 92 buoys with more than 2500 acquisitions. The validated SSP allows automatic processing of worldwide S1 IW images in VV or HH polarization, including Atlantic storms, cyclones, and huge storms in the Gulf of Alaska with a root-mean-square (RMSE) error of 80 cm for H s . For the closed seas like the North Sea, Baltic Seas and Black Sea the accuracy is higher with an RMSE = 55 cm. The algorithm is integrated into a demonstration service, used for further validation at the DLR ground station in Neustrelitz. The NRT processing has been tested by supporting a research ship cruise in the Antarctic Sea. ARTICLE HISTORY

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“…Computer vision for ocean imagery is a growing research area for detection of objects-of-interest, such as ships, aircraft, islands, clouds, and oil spills [1], [2], [3].…”

Section: This Page Intentionally Left Blank Introductionmentioning

confidence: 99%

Generalized hough transform for object classification in the maritime domain

Rerkngamsanga¹,

Tummala

Scrofani

et al. 2016

2016 11th System of Systems Engineering Conference (SoSE)

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Public reporting burden for this collection of information is estimated to average 1 hour per response, including the time for reviewing instruction, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing the collection of information. FUNDING NUMBERS AUTHOR(S) Pornrerk Rerkngamsanga PERFORMING ORGANIZATION NAME(S) AND ADDRESS(ES)Naval Postgraduate School Monterey, CA 93943-5000 PERFORMING ORGANIZATION REPORT NUMBER 9. SPONSORING /MONITORING AGENCY NAME(S) AND ADDRESS(ES)N/A SPONSORING / MONITORING AGENCY REPORT NUMBER11. SUPPLEMENTARY NOTES The views expressed in this thesis are those of the author and do not reflect the official policy or position of the Department of Defense or the U.S. Government. IRB Protocol number ____N/A____. 12a. DISTRIBUTION / AVAILABILITY STATEMENTApproved for public release; distribution is unlimited 12b. DISTRIBUTION CODE ABSTRACT (maximum 200 words)A Generalized Hough Transform (GHT)-based classification scheme for an object-of-interest in maritime-domain images is proposed in this thesis. First, the object edge points are extracted and used to generate a representation of the object as a Hough coordinate table by using the GHT algorithm. The table is then reformatted to a contour map called a Hough features map. The coordinates of dominant peaks or Hough features on the map are extracted and fed into a feed-forward, back-propagation neural network for classification. In this research, the scheme is tested using perfect shapes of triangles, squares, circles, and stars and maritime-domain images of ships, aircraft, and clouds, and the classification results obtained are reported. SUBJECT TERMS

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Near Real Time Applications for Maritime Situational Awareness

Cited by 19 publications

References 7 publications

Meteo-Marine Parameters from Sentinel-1 SAR Imagery: Towards Near Real-Time Services for the Baltic Sea

Meteo-Marine Parameters from Sentinel-1 SAR Imagery: Towards Near Real-Time Services for the Baltic Sea

Estimation of sea state from Sentinel-1 Synthetic aperture radar imagery for maritime situation awareness

Generalized hough transform for object classification in the maritime domain

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