Sea ice classification through statistical and textural analysis of passive millimeter radiometric data

• Main Author: Olson, Michael R.
• Other Authors: Wong, Robert, Swinford, Wayne, Dombourian, Edward
• Format: Master Thesis
• Language: English
• Published: California State University, Northridge 1994
• Subjects: Dissertations; Academic > CSUN > Engineering; Arctic; Sea ice
• Online Access: http://hdl.handle.net/10211.3/183414

Studies of Arctic sea ice have been performed using passive Ka band (33.6 GHz) radiometry and high-resolution photography. It was shown that two major ice categories, second-year/multi-year ice and first-year/young ice, could be classified in open water with the aid of the mean brightness temperature developed by radiometry. However, ice types within these major categories such as nilas and new ice could not classified by the mean brightness temperature alone. The ice types can be classified by the mean brightness temperature of the radiometry along with analysis of high-resolution photography. This study involved the development of techniques and methods of automated classification of sea ice in open water. Six types of ice: frazil, nilas, young, first-year, second-year, and multi-year are to be classified using digitized data derived from passive Ka microwave radiometric images. A pattern recognition scheme is developed to separate the ice classes by statistical analysis involved means, variances, and skews. Texture analysis of the passive microwave images was also needed to separate the ice data into ice classes. Several forms of filtering were performed along with edge detection schemes for textural classification. An automated pattern recognition program that combined the results of the statistical and textual analysis is used in the ice classification. Ice field data recorded in video format on Betamax tapes were processed and applied to the pattern recognition programs. Test results indicated the classification appeared to be successful in all cases, except in boundary regions. Includes bibliographical references (leaves 62-63) California State University, Northridge. Department of Engineering.