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“Image Techniques for Identifying Sea-Ice Parameters”

Authors: Qin Zhang and Roger Skjetne,
Affiliation: NTNU, Department of Marine Technology
Reference: 2014, Vol 35, No 4, pp. 293-301.

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Keywords: Image processing; Sea-ice; ice concentration Floe size distribution; Aerial sea-ice image; Fisheye sea-ice image; 360 degree camera system

Abstract: The estimation of ice forces are critical to Dynamic Positioning (DP) operations in Arctic waters. Ice conditions are important for the analysis of ice-structure interaction in an ice field. To monitor sea-ice conditions, cameras are used as field observation sensors on mobile sensor platforms in Arctic. Various image processing techniques, such as Otsu thresholding, k-means clustering, distance transform, Gradient Vector Flow (GVF) Snake, mathematical morphology, are then applied to obtain ice concentration, ice types, and floe size distribution from sea-ice images to ensure safe operations of structures in ice covered regions. Those techniques yield acceptable results, and their effectiveness are demonstrated in case studies.

PDF PDF (2567 Kb)        DOI: 10.4173/mic.2014.4.6

DOI forward links to this article:
  [1] Roger Skjetne, Lars Imsland and Sveinung Løset (2014), doi:10.4173/mic.2014.4.1
  [2] (2017), doi:10.3390/rs9090930

[1] Banfield, J. (1991). Automated tracking of ice floes: A stochastic approach, Geoscience and Remote Sensing, IEEE Transactions on. 29(6):905--911. doi:10.1109/36.101369
[2] Banfield, J.D. and Raftery, A.E. (1992). Ice floe identification in satellite images using mathematical morphology and clustering about principal curves, Journal of the American Statistical Association. 87(417):7--16. doi:10.1080/01621459.1992.10475169
[3] Basak, S.C., Magnuson, V., Niemi, G., and Regal, R. (1988). Determining structural similarity of chemicals using graph-theoretic indices, Discrete Applied Mathematics. 19(1):17--44. doi:10.1016/0166-218X(88)90004-2
[4] Bird, K.J. (2008). Circum-Arctic resource appraisal: Estimates of undiscovered oil and gas north of the Arctic Circle, US Department of the Interior, US Geological Survey.
[5] Bjorklund, H., Prusakov, A., and Sinitsyn, A. (2013). 360 degree camera system, Technical report, Department of Civil and Transport Engineering, Norwegian University of Science and Technology.
[6] Blunt, J., Garas, V., Matskevitch, D., Hamilton, J., and Kumaran, K. (2012). Image analysis techniques for high Arctic, deepwater operation support, In OTC Arctic Technology Conference. Houston, Texas, USA, 2012.
[7] Gonzalez, R.C., Woods, R.E., and Eddins, S.L. (2003). Digital Image Processing Using MATLAB, Prentice-Hall, Inc., Upper Saddle River, NJ, USA. doi:10.1115/OMAE2012-83860
[8] GoPro. (0). HD HERO2 PROFESSIONAL, accessed 2014-11-22. http://gopro.com/product-comparison-hd-hero2-hd-hero-cameras.
[9] Gurtner, A. (2009). Experimental and numerical investigations of ice-structure interaction, Ph.D. thesis, Norwegian University of Science and Technology.
[10] Hamilton, J., Holub, C., Blunt, J., Mitchell, D., Kokkinis, T., etal. (2011). Ice management for support of arctic floating operations, In OTC Arctic Technology Conference. Offshore Technology Conference.
[11] Haugen, J., Imsland, L., Loset, S., and Skjetne, R. (2011). Ice observer system for ice management operations, In Proceeding of the 21st International Ocean and Polar Engineering Conference. Maui, Hawaii, USA.
[12] Ji, S., Li, H., Wang, A., and Yue, Q. (2011). Digital image techniques of sea ice field observation in the bohai sea, In POAC11-077, Proceedings of the 21st International Conference on Port and Ocean Engineering under Arctic Conditions (POAC’11), Montreal, Canada. 2011.
[13] Keinonen, A. (2008). Ice management for ice offshore operations, In Proceedings of the Offshore Technology Conference. Houston, TX. 2008.
[14] Keinonen, A., Wells, H., Dunderdale, P., Pilkington, R., Miller, G., Brovin, A., etal. (2000). Dynamic positioning operation in ice offshore sakhalin may-june 1999, In The Tenth International Offshore and Polar Engineering Conference. International Society of Offshore and Polar Engineers.
[15] MacQueen, J. etal. (1967). Some methods for classification and analysis of multivariate observations, In Proceedings of the fifth Berkeley symposium on mathematical statistics and probability, volume1. California, USA, page14.
[16] Makrygiannis, A. (2012). Design and Simulation of an Ice-Capable DP System, Master's thesis, Norwegian University of Science and Technology, The address of the publisher.
[17] Nguyen, D.T., Sorbo, A., and Soerensen, A. (2009). Modelling and control for dynamic positioned vessels in level ice, In Manoeuvring and Control of Marine Craft. pages 229--236, 2009. doi:10.3182/20090916-3-BR-3001.00036
[18] Otsu, N. (1975). A threshold selection method from gray-level histograms, Automatica. 11(285-296):23--27. doi:10.1109/TSMC.1979.4310076
[19] Rosenfeld, A. and Pfaltz, J.L. (1968). Distance functions on digital pictures, Pattern Recognition. 1(1):33--61. doi:10.1016/0031-3203(68)90013-7
[20] Soh, L.-K., Tsatsoulis, C., and Holt, B. (1998). Identifying ice floes and computing ice floe distributions in SAR images, In Analysis of SAR Data of the Polar Oceans, pages 9--34. Springer.
[21] Toyota, T. and Enomoto, H. (2002). Analysis of sea ice floes in the sea of okhotsk using ADEOS/AVNIR images, In Proceedings of the 16th IAHR International Symposium on Ice, Dunedin, New Zealand. pages 211--217.
[22] Xu, C. and Prince, J.L. (1998). Snakes, shapes, and gradient vector flow, IEEE Transactions on Image Processing. 7(3):359--369. doi:10.1109/83.661186
[23] Zhang, Q. and Skjetne, R. (2014). Image processing for identification of sea-ice floes and the floe size distribution, Submit to IEEE Transactions on Geoscience and Remote Sensing, 2014.
[24] Zhang, Q., Skjetne, R., Loset, S., and Marchenko, A. (2012). Digital image processing for sea ice observation in support to Arctic DP operation, In Proceedings of 31st International Conference on Ocean, Offshore and Arctic Engineering, OMAE2012-83860. ASME, Rio de Janeiro, Brazil, 2012.
[25] Zhang, Q., Skjetne, R., Metrikin, I., and Loset, S. (2014). Image processing for ice floe analyses in broken-ice model testing, Submit to Cold Region Science and Technology.
[26] Zhang, Q., Skjetne, R., and Su, B. (2013). Automatic image segmentation for boundary detection of apparently connected sea-ice floes, In Proceedings of the 22nd International Conference on Port and Ocean Engineering under Arctic Conditions. Espoo, Finland.
[27] Zhang, Q., vander Werff, S., Metrikin, I., Loset, S., and Skjetne, R. (2012). Image processing for the analysis of an evolving broken-ice field in model testing, In Proceedings of 31st International Conference on Ocean, Offshore and Arctic Engineering, OMAE2012-84117. ASME, Rio de Janeiro, Brazil, 2012. doi:10.1115/OMAE2012-84117

  title={{Image Techniques for Identifying Sea-Ice Parameters}},
  author={Zhang, Qin and Skjetne, Roger},
  journal={Modeling, Identification and Control},
  publisher={Norwegian Society of Automatic Control}


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