MAGIC: MAp-Guided Ice Classification System

Canadian Journal of Remote Sensing

Volumn 36, Issue , 2010, Pages S13-S25

  Clausi, D A ^a   Qin, A K ^a   Chowdhury, M S ^a   Yu, P ^a   Maillard, P ^b  

^a UNIVERSITY OF WATERLOO   (Canada)

^b FEDERAL UNIVERSITY OF MINAS GERAIS   (Brazil)

Author keywords

[No Author keywords available]

Indexed keywords

ANCILLARY DATA; CANADIAN ICE SERVICES; DIGITAL IMAGERY; ICE CHARTS; ICE CLASSIFICATION; ICE CONCENTRATION; SAR IMAGES; SENSOR RESOLUTION; SPECIFIC TOOL;

DESIGN; IMAGE SEGMENTATION; IMAGING SYSTEMS; OCEANOGRAPHY; SEA ICE;

SYNTHETIC APERTURE RADAR;

EID: 77956149505     PISSN: 07038992     EISSN: 17127971     Source Type: Journal    
DOI: 10.5589/m10-008     Document Type: Article

References (30)

1. Bertoia, C., and Ramsay, B. 1998. Sea ice analysis and products: cooperative work at the U.S. and Canadian national ice centers. In IGARSS’98, Proceedings of the IEEE International Geoscience and Remote Sensing Symposium, 6-10 July 1998, Seattle, Wash. IEEE, New York. pp. 1944-1947.
2. Carsey, F. 1989. Review and status of remote sensing of sea ice. IEEE Journal of Oceanic Engineering, Vol. 41, No. 2, pp. 127-138.
3. Clausi, D.A. 2001. Comparison and fusion of co-occurrence, Gabor, and MRF texture features for classification of SAR sea ice imagery. Atmosphere and Oceans, Vol. 39, No. 4, pp. 183-194.
4. Clausi, D.A., and Jernigan, M.E. 1998. A fast method to determine cooccurrence texture features. IEEE Transactions on Geosciences and Remote Sensing, Vol. 36, No. 1, pp. 298-300.
5. Clausi, D.A., and Jernigan, M.E. 2000. Designing Gabor filters for optimal texture separability. Pattern Recognition, Vol. 33, No. 11, pp. 1835-1849.
6. Deng, H., and Clausi, D.A. 2001. Mapping seasonal flooding in forested wetlands using multi-temporal RADARSAT SAR. Photogrammetric Engineering & Remote Sensing, Vol. 67, No. 7, pp. 857-864.
7. Deng, H., and Clausi, D.A. 2004a. Gaussian MRF rotation-invariant features for image classification. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 26, No. 7, pp. 951-955.
8. Deng, H., and Clausi, D.A. 2004b. Unsupervised image segmentation using a simple MRF model with a new implementation scheme. Pattern Recognition, Vol. 37, No. 12, pp. 2323-2335.
9. Deng, H., and Clausi, D.A. 2005. Unsupervised segmentation of synthetic aperture radar sea ice imagery using a novel Markov random field model. IEEE Transactions on Geoscience and Remote Sensing, Vol. 43, No. 3, pp. 528-538.
10. Duda, R.O., Hart, P.E., and Stork, D.G. 2000. Pattern classification. 2nd ed. Wiley-Interscience, Malden, Mass.
11. Fetterer, C.B., and Ye, J. 1997. Multi-year ice concentration from radarsat. In IGARRS’97, Proceedings of the IEEE International Geoscience and Remote Sensing Symposium, 3-8 August 1997, Singapore. IEEE, New York. pp. 402-404.
12. Hall, D. 1998. Remote sensing of snow and ice using imaging radar. In Manual of remote sensing. Volume 2. Principles and applications of imaging radar. Edited by F.M. Henderson and A.J. Lewis. 3rd ed. Wiley, New York. pp. 677-703.
13. Haverkamp, D., Soh, L.K., and Tsatsoulis, C. 1993. A dynamic local thresholding technique for sea ice classification. In IGARRS’93, Proceedings of the IEEE International Geoscience and Remote Sensing Symposium, 18-21 August 1993, Tokyo, Japan. Edited by S. Fujimura. IEEE, New York. pp. 638-640.
14. Karvonen, J.A. 2004. Baltic sea ice SAR segmentation and classification using modified pulse-coupled neural networks. IEEE Transactions on Geoscience and Remote Sensing, Vol. 42, No. 7, pp. 1566-1574.
15. Li, S.Z. 2001. Markov random field modeling in computer vision. 2nd ed. Springer-Verlag, New York.
16. Liu, H., and Motoda, H. 1998. Feature extraction, construction and selection: a data mining perspective. 1st ed. Springer, New York.
17. Maillard, P., Clausi, D.A., and Deng, H. 2005. Map-guided sea ice segmentation and classification using SAR imagery and a MRF segmentation scheme. IEEE Transactions on Geoscience and Remote Sensing, Vol. 43, No. 12, pp. 2940-2951.
18. Panjwani, D.K., and Healey, G. 1995. Markov random field models for unsupervised segmentation of textured color images. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 17, No. 10, pp. 939-954.
19. Press, W.H., Flannery, B.P., Teukolsky, S.A., and Vetterling, W.T. 1992. Numerical recipes in C: the art of scientific computing. 2nd ed. Cambridge University Press, Cambridge, U.K.
20. Ramsay, B., Flett, D., Andersen, H.S., Gill, R., Nghiem, S., and Bertoia, C. 2004. Preparation for the operational use of RADARSAT-2 for ice monitoring. Canadian Journal of Remote Sensing, Vol. 30, No. 3, pp. 415-423.
21. Samadani, R. 1995. A finite mixture algorithm for finding proportions in SAR images. IEEE Transactions on Image Processing, Vol. 4, No. 8, pp. 1182-1186.
22. Snyder, J.P. 1987. Map projections — a working manual. US Government Printing Office, Washington, D.C.
23. Soh, L.K., and Tsatsoulis, C. 1999. Unsupervised segmentation of ERS and radarsat sea ice images using multiresolution peak detection and aggregated population equalization. International Journal of Remote Sensing, Vol. 20, No. 15-16, pp. 3087-3109.
24. Soh, L.K., Tsatsoulis, C., Gineris, D., and Bertoia, C. 2004. ARKTOS: an intelligent system for SAR sea ice classification. IEEE Transactions on Geoscience and Remote Sensing, Vol. 42, No. 1, pp. 229-248.
25. Tomasi, C., and Manduchi, R. 1998. Bilateral filtering for gray and color images. In Proceedings of the 6th International Conference on Computer Vision (ICCV98), 4-7 January 1998, Bombay, India. Edited by S. Chandran and U. Desai. Narosa Publishing House, New Delhi, India. pp. 839-846.
26. Vincent, L., and Soille, P. 1991. Watersheds in digital spaces: an efficient algorithm based on immersion simulations. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 13, No. 6, pp. 583-598.
27. th Canadian Conference on Computer and Robot Vision (CVR 2009), 2527 May 2009, Kelona, B.C. IEEE Computer Society, Los Alamitos, Calif.
28. Yang, X.Z., and Clausi, D.A. 2007. SAR sea ice image segmentation based on edge-preserving watersheds. In Proceedings of the 4th Annual Canadian Conference on Computer and Robot Vision (CVR 2007), 2830 May 2007, Montreal, Que. IEEE Computer Society, Los Alamitos, Calif. pp. 426-431.
29. Yu, Q., and Clausi, D.A. 2005. SAR sea-ice image analysis based on iterative region growing using semantics. IEEE Transactions on Geoscience and Remote Sensing, Vol. 45, No. 12, pp. 3919-3931.
30. Yu, Q., and Clausi, D.A., 2008. IRGS: image segmentation using edge penalties and region growing. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 30, No. 12, pp. 2126-2139.