A multi-level SAR sea ice image classification method by incorporating egg-code-based expert knowledge

2012 5th International Congress on Image and Signal Processing, CISP 2012

Volumn , Issue , 2012, Pages 982-986

  Wang, Tan ^a   Yang, Xuezhi ^a   Wang, Yujie ^a   Fang, Jing ^a   Jia, Li ^a  

^a HEFEI UNIVERSITY OF TECHNOLOGY   (China)

Author keywords

expert knowledge; Markov random field (MRF); sea ice classification; sea ice segmentation; synthetic aperture radar (SAR)

Indexed keywords

CLASSIFICATION METHODS; CLIMATIC RESEARCH; EUCLIDEAN DISTANCE; EXPERT KNOWLEDGE; MARKOV RANDOM FIELDS; REFERENCE VECTORS; SEA ICE CLASSIFICATION; SHIP NAVIGATION;

MARKOV PROCESSES; SEA ICE; SIGNAL PROCESSING; SYNTHETIC APERTURE RADAR;

IMAGE CLASSIFICATION;

EID: 84875048442     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CISP.2012.6469789     Document Type: Conference Paper

References (13)

1. Q.Yu and D. A. Clausi, "SAR sea-ice image analysis based on iterative region growing using semantics," IEEE Transactions on Geoscience and Remote Sensing, vol.43, no.12, pp. 3919-3931,2007 (Pubitemid 350157829)
2. World Meteorology Organization, Dec. 17, 2005, [Online]. Available: http://www.wmo.ch/index-en.html
3. D.Barber and E.LeDrew, "SAR sea ice discrimination using texture statistics: A multivariate approach," Photogrammetric Engineering and Remote Sensing, vol.57, no.4, pp.385-395, 1991
4. A. Bogdanov, S. Sandven, O. Johannessen, V. Alexandrov, and L. Bobylev, "Multisensor approach to automated classification of sea ice image data," IEEE Transactions on Geoscience and Remote Sensing, vol.43, no.7, pp.1648-1664,2005 (Pubitemid 41164674)
5. D.A. Clausi, "Comparison and fusion of co-occurrence, Gabor and MRF Texture Features for Classification of SAR Sea-Ice Imagery," Atmosphere and Oceans, vol.39, no. 3, pp. 183-194, 2001
6. Y. Yu and S. T. Acton, "Speckle reducing anisotropic diffusion", IEEE Transactions Image Processing, 11(11), pp. 1260-1270, 2002
7. L. Vincent and P. Solille, "Watershed in digital spaces: an efficient algorithm based on immersion simulations", IEEE Transactions Pattern Anal. Mach. Intell. 13(6), pp. 583-598, June, 1991 (Pubitemid 21675616)
8. X. Z. Yang and D. A. Clausi, "SAR sea ice image segmentation using an edge-preserving region-based MRF", Proc. Of IEEE Int'l Conf. on Image Processing (ICIP09), pp. 1721-1724, Cairo, Egypt, 2009
9. S. Li, Markov Random Field Modeling in Computer Vision. Springer, New York, 2009
10. J. A. Karvonen, "Baltic sea ice SAR segmentation and classification using modified pulse-coupled neural networks", IEEE Transactions Geosci. Remote Sens. Lett., vol.2, no. 3, pp.275-279, Jul.2005
11. P. Maillard, D. A. Clausi, and H. Deng, "Operational map-guided classification of SAR sea ice imagery", IEEE Trans. Geosci, Remote Sens., vol. 43, no.12, pp.2940-2951, Dec.2005 (Pubitemid 41788558)
12. L. K. Soh, C. Tsatsoulis, D. Gineris, and C. Bertoia, "ARKTOS: An intelligent system for SAR sea ice image classification", IEEE Transactions Geosci. Remote Sens., vol.42,no 1, pp. 229-248, Jan.2004
13. S. Ochilov, D. A. Clausi, "Automated classification of operational SAR sea ice images", 6th Canadian Conference on Computer and Robot Vision (CRV), pp. 40-46, Ottawa, Ontario, Canada, May 31-June 2, 2012