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Evaluation of spectral classifiers for separating Sea Ice 
from open water in preparation for Radarsat. 
T. Heacock!, T. Hirose!, M. Manore? 
  
1. Noetix Research Inc. 9^ Floor, 280 Albert St. Ottawa, Ontario, Canada, K1P 5G8. 
2. Canada Centre for Remote Sensing (CCRS), Ice Applications Group, 588 Booth St, Ottawa, Ontario, Canada, 
K1A 0Y7. 
  
ABSTRACT 
The 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 Environment 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 basis 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. 
KEY WORDS 
Synthetic Aperture Radar, Sea Ice, Classification, Algorithm, Tone, Texture. 
Environment Canada (ICEC), and it's client services 
1.0 INTRODUCTION 
Remotely sensed data is a practical and cost-effective 
method of acquiring detailed, timely information over 
Canada's ice-infested waters. With the launch of 
RADARSAT in early 1995 (Langham 1992), large 
volumes of Synthetic Aperture Radar (SAR) data will 
be acquired over Canada's oceans. It is anticipated 
that manual analysis of the data on a regional scale 
will be overwhelming. To overcome this potential 
bottleneck, researchers have proposed the 
development of automated systems to generate 
products for end users in all applications disciplines, 
and in particular for sea ice applications because of 
the operational nature of the Ice Centre, 
(Ramsay et al. 1993). 
An automated system for classifying Radarsat 
imagery must be able operate on data acquired from 
a single channel, single polarized SAR (C-band hh). 
The system must be robust enough to operate on 
imagery illustrating a diversity of ice conditions, 
collected from all seasons and all geographic regions 
of operational interest to ICEC. Furthermore, the 
data product which will be used by ICEC operations 
will be ScanSAR data, generated through a quick 
delivery system. This data will contain image 
ambiguities that are a function of the unique nature of 
the ScanSAR image creation process. 
To date, ice classification research has been carried 
out for both calibrated (Kwok et al. 1992) and 
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