1073
AUTOMATED NEAR REAL TIME RADARSAT-2 IMAGE GEO-PROCESSING
AND ITS APPLICATION FOR SEA ICE AND OIL SPILL MONITORING
Ziqiang Ou
Canadian Ice Service, Environment Canada
373 Sussex Drive, Ottawa, Canada K1A 0H3 - Ziqiang.Ou@ec.gc.ca
KEY WORDS: RADARSAT, SAR, Georeferencing, Real-time, Pollution, Snow Ice, Detection
ABSTRACT:
Synthetic aperture radar (SAR) data from RADARSAT-1 are an important operational data source for several ice centres around the
world. Sea ice monitoring has been the most successful real-time operational application for RADARSAT-1 data. These data are
used by several national ice services as a mainstay of their programs. Most recently, Canadian Ice Service launched another new
real-time operational program called ISTOP (Integrated Satellite Tracking Of Pollution). It uses the RADARSAT-1 data to monitor
ocean and lakes for oil slicks and tracking the polluters. In this paper, we consider the ice information requirements for operational
sea ice monitoring and the oil slick and target detection requirements for operational ISTOP program at the Canadian Ice Service
(CIS), and the potential for RADARSAT-2 to meet those requirements. Primary parameters are ice-edge location, ice concentration,
and stage of development; secondary parameters include leads, ice thickness, ice topography and roughness, ice decay, and snow
properties. Iceberg detection is an additional ice information requirement. The oil slick and ship target detection are the basic
requirements for the surveillance and pollution control.
1. INTRODUCTION
RADARSAT-2 products are provided in a GeoTIFF format
with a sets of support XML files for all products except the
RAW. The GeoTIFF images are georeferenced, but not
geocorrected so that they are not ready for most of GIS and
Remote Sensing applications, which require the RADARSAT-2
images being overlaid with other geographic data layers. Since
the products are not geographically corrected, the geographic
metadata included in GeoTIFF is limited to a set of points tying
image location to geographic location. GeoTIFF images is
generated in TIFF strip format. Multipolar images is generated
as separate GeoTIFF image files. All images are oriented such
that north is nominally up and east is nominally on the right.
Further processing is required in order to integrate the dataset
into CIS existing operational Integrated Spatial Information
System - ISIS (Ou, 2004; Koonar et al. 2004) and the
Integrated Satellite Tracking Of Pollution - ISTOP system
(Gauthier et al. 2007).
Both ISIS for ice monitoring and ISTOP for oil monitoring are
real-time or near real-time applications. The data acquisition
and the data processing time is one of the critical factors.
Considering the data volume of SAR images and in order to
meet the time requirements, an automated and fast SAR image
processing and geo-referencing algorithm is the key for the
success of both applications. Thus, this paper will discuss the
automated fast RADARSAT-2 image geo-processing by using
the image geographical tie points provided in the RADARSAT-
2 product; the evaluation of geographical location accuracy for
the geocoded image; the evaluation of image geo-spatial
distortion introduced by geo-processing and how to minimize
the distortion and improve the image’s geo-location accuracy.
A set of different geo-reference procedures will be evaluated
based on their spatial accuracy of geocoded image and their
computational efficiencies in order to meet the requirements for
the mapping accuracy of ice and oil and the time constraint of
these real-time applications.
2. GEOMETRIC TRANSFORMATION
There are two major techniques for correcting geometric
distortion present in an image. The first is to model the nature
and magnitude of the sources of distortion and then use these
models to correct for distortions. The second is to establish a
mathematical relationship between the locations of pixels in an
image (X,Y) and their location (U,V) in the real world (e.g. on a
map). The latter is by far the most common, though many
standard sources of satellite images will have at least some
corrections done by modelling before the user receives the data.
The idea is to develop a mathematical function that relates the
image and real world coordinate systems, e.g. X = f(U, V) and
Y = g(U, V). While in the end we want to transform our images
in the pixel coordinate system into some coordinate system in
the real world, in actuality this is accomplished in reverse. Once
a real world coordinate system is established, the mapping
functions (f, g) are used to determine which pixels in the image
whose centers fall closest to the locations of the real-world grid.
The transformation from a image coordinate system to a real
world coordinate system through a set of tie points of the two
coordinate systems can be mathematically expressed by a set of
mapping functions with respect to two coordinate components.
The coordinate mapping from any point [u, v] in the image
coordinate system to the corresponding point [x, y] in the real
world coordinate system is modelled as:
x = f(u,v/a)
y = g(u,v/fi) (0
where a and P are parameters of the mapping function f and g
respectively and are determined by control points, and are
commonly determined by a global transformation.
