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TitleEvaluation of spectral classifiers for separating sea ice from open water in preparation for RADARSAT
DownloadDownload (whole publication)
LicencePlease note the adoption of the Open Government Licence - Canada supersedes any previous licences.
AuthorHeacock, T; Hirose, T; Manore, M
SourceISPRS Commission II Symposium: systems for data processing, analysis and presentation; International archives of photogrammetry and remote sensing vol. 30, pt. 2, 1994 p. 427-435,
Alt SeriesEarth Sciences Sector, Contribution Series 2005724
PublisherNatural Resources Canada
MeetingISPRS Commission II Symposium: Systems for Data Processing, Analysis and Presentation; Ottawa, ON; CA; June 6-10, 1994
Mediapaper; digital; on-line
RelatedThis publication is contained in ISPRS Commission II Symposium: systems for data processing, analysis and presentation
Subjectsremote sensing; oceanography; sea ice; surface waters; satellites; satellite imagery; textures; Classification
Released2019 01 01
AbstractThe launch of Radarsat in 1995 confirms Canada's commitment to the use of spaceborne radar data for the monitoring of its land and ocean areas. Large volumes of data will be used by the sea ice community, and in particular the Ice Centre Envioronment Canada to monitor the shipping lanes in and around Canada. In an effort to assist in data analysis automated systems are being developed to extract value-added information products for end users. To date, research has been conducted on methods of extracting ice information from both calibrated and uncalibrated SAR data. The first step in the development of a fully automated system is the separation of areas of ice and water. Five algorithms that use only tone and local scene texture extracted from an uncalibrated SAR scene (so called spectral algorithms) were evaluated. These algorithms, representative of all spectral algorithms, were selected because of their computational speed and efficiency. The results illustrated that all the algorithms have the ability to separate ice from water in SAR imagery under ideal conditions. The performance varied on an image to image bases dependant on the amount of systematic or geophysical variability within each scene used for the evaluation. Observations about the relative strengths and weaknesses of the algorithms are made.

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