Ship-iceberg detection & classification in sentinel-1 SAR images

• Autorzy: Heiselberg H.

Identyfikatory

DOI

10.12716/1001.14.01.30

• Języki publikacji: EN

Abstrakty

EN

The European Space Agency Sentinel-1 satellites provide good resolution all weather SAR images. We describe algorithms for detection and classification of ships, icebergs and other objects at sea. Sidelobes from strongly reflecting objects as large ships are suppressed for better determination of ship parameters. The resulting improved ship lengths and breadths are larger than the ground truth values known from Automatic Identification System (AIS) data due to the limited resolution in the processing of the SAR images as compared to previous analyses of Sentinel-2 optical images. The limited resolution in SAR imagery degrades spatial classification algorithms but it is found that the backscatter horizontal and vertical polarizations can be exploited to distinguish icebergs in the Arctic from large ships but not small boats or wakes.

Słowa kluczowe

EN

arctic shipping; ship-iceberg detection; sentinel-1; synthetic aperture radar; SAR; European Space Agency (ESA); SAR Imagery; copernicus program; ground range detected; high resolution (GRDH)

• Wydawca: Faculty of Navigation, Gdynia Maritime University
• Czasopismo: TransNav : International Journal on Marine Navigation and Safety of Sea Transportation
• Rocznik: 2020
• Tom: Vol. 14, no. 1
• Strony: 235--241
• Opis fizyczny: Bibliogr. 15 poz., rys., tab.

Twórcy

autor

Heiselberg H.

• Technical University of Denmark, Lyngby, Denmark

Bibliografia

• [1] ESA Copenernicus Program, Sentinel Scientific Data Hub. https://sentinel.esa.int/web/sentinel/user-guides/ sentinel-1-sar/document-library/-/asset_publisher/ 1dO7RF5fJMbd/content/sentinel-1-product-definition
• [2] C-CORE. “Summary of previous research in iceberg and ship detection and discrimination in SAR,” DRDC report no: R-13-060-1098, 2013.
• [3] G. Saur, M. Teutsch, “SAR signature analysis for TerraSAR-X-based ship monitoring,” Image and Signal Processing for Remote sensing XVI. Proc. of SPIE Vol. 7830, pp. 78301O-1, 2010.
• [4] C. Bentes, D. Velotto, S. Lehner, “Analysis of ship size detectability over different TerraSAR-X modes,” IGARSS IEEE, pp. 5137, 2014.
• [5] S. Brusch, S. Lehner, T. Fritz, M. Soccorsi, A. Soloviev, B.v. Schie, “Ship surveillance with TerraSAR-X,” IEEE Transactions on Geoscience and Remote Sensing, 2010.
• [6] S. Brusch, S. Lehner, E. Schwarz, T. Fritz, “Near real time ship detection experiments,” Proc. SeaSAR 2010, Frascati. ESA SP-679, 2010.
• [7] D. Velotto, M. Migliaccio, S. Lehner, “DualPolarimetric TerraSAR-X SAR data for target at sea observation,” IEEE Geoscience and Remote Sensing Letters, Vol. 10, p. 1114, 2013.
• [8] C. Brekke, D.J. Weydahl, Ø. Helleren, R. Olsen, “Ship traffic monitoring using multipolarisation satellite SAR images combined with AIS reports,” Proc. of the 7th European Conference on Synthetic Aperture Radar (EUSAR), Friedrichshafen, Germany, 2–5 June, 2008.
• [9] C. Santamaria, H. Greidanus, M. Fournier, T. Eriksen, M. Vespe, M. Alvarez, V.F. Arguedas, C. Delaney, P. Argentieri, “Sentinel-1 contribution to monitoring maritime activity in the Arctic,” Proc. Living Planet Symposium 2016, Prague, ESA SP-740, 2016.
• [10] M. Stasolla, H. Greidanus, “The exploitation of Sentinel-1 images for vessel size estimation,” Remote Sens. Lett., vol. 7, no. 12, pp. 1219-1228, 2016
• [11] G. Hajduch, N. Longepe, J. Hanonneau, J.Y. Le Bras, “Progress in automatic ship detection and classification,” Proc. SEASAR 2012, Tromso, Norway, 2012, ESA SP-709 2013, id. 19
• [12] H. Heiselberg, “A direct and fast methodology for ship recognition in Sentinel-2 multispectral imagery by supervised classification,” Remote Sens., vol. 8, pp. 1033, 2016. https://doi.org/10.3390/rs8121033
• [13] P. Heiselberg, H. Heiselberg, “Ship-Iceberg discrimination in Sentinel-2 multispectral imagery,” Remote Sens., vol. 9(11), pp. 1156, 2017. https://doi.org/10.3390/rs9111156
• [14] C. Bentes, A. Frost, D. Velotto, B. Tings, “ShipIceberg discrimination with convolutional neural networks in high resolution SAR images,” Proc. of EUSAR, 2016.
• [15] Iceberg Classifier Challenge 2018 in Machine Learning, https://www.kaggle.com/c/statoil-iceberg-classifierchallenge

Uwagi

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020).