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AUTOMATED NEAR REAL TIME RADARSAT-2 IMAGE GEO-PROCESSING and ITS APPLICATION for SEA ICE and OIL SPILL MONITORING )/,( )/,( Vugy

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AUTOMATED NEAR REAL TIME RADARSAT-2 IMAGE GEO-PROCESSING and ITS APPLICATION for SEA ICE and OIL SPILL MONITORING )/,( )/,( Vugy 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 - [email protected] 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 2. GEOMETRIC TRANSFORMATION RADARSAT-2 products are provided in a GeoTIFF format There are two major techniques for correcting geometric with a sets of support XML files for all products except the distortion present in an image. The first is to model the nature RAW. The GeoTIFF images are georeferenced, but not and magnitude of the sources of distortion and then use these geocorrected so that they are not ready for most of GIS and models to correct for distortions. The second is to establish a Remote Sensing applications, which require the RADARSAT-2 mathematical relationship between the locations of pixels in an images being overlaid with other geographic data layers. Since image (X,Y) and their location (U,V) in the real world (e.g. on a the products are not geographically corrected, the geographic map). The latter is by far the most common, though many metadata included in GeoTIFF is limited to a set of points tying standard sources of satellite images will have at least some image location to geographic location. GeoTIFF images is corrections done by modelling before the user receives the data. generated in TIFF strip format. Multipolar images is generated as separate GeoTIFF image files. All images are oriented such The idea is to develop a mathematical function that relates the that north is nominally up and east is nominally on the right. image and real world coordinate systems. e.g. X = f(U, V) and Further processing is required in order to integrate the dataset Y = g(U, V). While in the end we want to transform our images into CIS existing operational Integrated Spatial Information in the pixel coordinate system into some coordinate system in System – ISIS (Ou, 2004; Koonar et al. 2004) and the the real world, in actuality this is accomplished in reverse. Once Integrated Satellite Tracking Of Pollution – ISTOP system a real world coordinate system is established, the mapping (Gauthier et al. 2007). functions (f, g) are used to determine which pixels in the image whose centers fall closest to the locations of the real-world grid. Both ISIS for ice monitoring and ISTOP for oil monitoring are real-time or near real-time applications. The data acquisition The transformation from a image coordinate system to a real and the data processing time is one of the critical factors. world coordinate system through a set of tie points of the two Considering the data volume of SAR images and in order to coordinate systems can be mathematically expressed by a set of meet the time requirements, an automated and fast SAR image mapping functions with respect to two coordinate components. processing and geo-referencing algorithm is the key for the The coordinate mapping from any point [u, v] in the image success of both applications. Thus, this paper will discuss the coordinate system to the corresponding point [x, y] in the real automated fast RADARSAT-2 image geo-processing by using world coordinate system is modelled as: the image geographical tie points provided in the RADARSAT- 2 product; the evaluation of geographical location accuracy for = vufx α)/,( the geocoded image; the evaluation of image geo-spatial (1) distortion introduced by geo-processing and how to minimize = vugy β)/,( the distortion and improve the image’s geo-location accuracy. A set of different geo-reference procedures will be evaluated where α and β are parameters of the mapping function f and g based on their spatial accuracy of geocoded image and their respectively and are determined by control points, and are computational efficiencies in order to meet the requirements for commonly determined by a global transformation. the mapping accuracy of ice and oil and the time constraint of these real-time applications. 1073 The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part B4. Beijing 2008 Since the 2D polynomial functions do not reflect the sources of ⎡α ⎤ ⎡β ⎤ distortion during the image formation and do not correct for 0 0 terrain relief distortions, they are limited to images with few or ⎢ ⎥ ⎢ ⎥ ⎡ε x ⎤ ⎡ε y ⎤ α1 β1 1 1 ⎢ ⎢ small distortions, such as nadir-viewing images, systematically- ⎢ ⎥ ⎢ ⎥ ⎥ ⎥ corrected images and/or small images over flat terrain. These ε x ε y ⎢α 2 ⎥ ⎢β 2 ⎥ ε = ⎢ 2 ⎥ , ε = ⎢ 2 ⎥ , A = ⎢ ⎥ , B = ⎢ ⎥ functions correct for local distortions at the ground control point x ⎢ # ⎥ y ⎢ # ⎥ α β (GCP) location. They are very sensitive to input errors and ⎢ ⎥ ⎢ ⎥ ⎢ 3 ⎥ ⎢ 3 ⎥ hence GCPs have to be numerous and regularly distributed. ⎢ ⎥ ⎢ ⎥ ⎢ε x ⎥ ⎢ε y ⎥ α β Consequently, these functions should not be used when precise ⎣ n ⎦ ⎣ n ⎦ ⎢ 4 ⎥ ⎢ 4 ⎥ geometric positioning is required for multi-source multi-format ⎣⎢α 5 ⎦⎥ ⎣⎢β5 ⎦⎥ data integration and in high relief areas. If a second order polynomial function is used, the following is obtained: 2 2 ⎡1 u1 v 1 u 1 v 1 u 1 v1 ⎤ ⎢ 2 2 ⎥ 2 2 ⎢1 u2 v 2 u 2 v 2 u 2 v2 ⎥ x=α0 + α 1 u + α 2 v + α 3 uv + α4 u + α 5 v W = (2) ⎢ ⎥ 2 2 ###### y=β0 + β 1 u + β 2 v + β 3 uv + β4 u + β 5 v ⎢ 2 2 ⎥ ⎣⎢1 un v n u n v n u n v n ⎦⎥ From a mathematical point of view, a first order polynomial x y transformation requires a minimum of 3 points, a second order ⎡ 1 ⎤ ⎡ 1 ⎤ polynomial requires a minimum of 6 points and, a third order ⎢ ⎥ ⎢ ⎥ x2 y2 polynomial requires a minimum of 10 points. Generally, if the X = ⎢ ⎥ , Y = ⎢ ⎥ order of polynomial model is n, we must at least have a set of ⎢ # ⎥ ⎢ # ⎥ M=(n+1)(n+2)/2 GCPs to solve Equations (1). Suppose that {(u , ⎢ ⎥ ⎢ ⎥ i x y vi): i = 1, …, n} and {(xi, yi): i=1, …, n} are the tying GCP ⎣ n ⎦ ⎣ n ⎦ pairs from image and real world coordinate systems respectively, we can form n equations for both x and y as following: This means that the two sets of coefficients αj and βj could be estimated separately. The least square estimation can be used to determine the coefficients of αj and βj from GCPs. x =α + αu + α v + α u v + α u2 + α v 2 + ε i 0 1 i2 i 3 i i 4 i 5 i x i y =β + βu + β v + β u v + β u2 + β v 2 + ε T ˆ T ˆ i 0 1 i2 i 3 i i 4 i 5 i y i Min[](εx ε x =WA − X)( WA − X ) (6) )Min 3[](εT ε =WB (ˆ − Y)(T WBˆ − Y) y y By assuming that the ε are independent and identically distributed (iid) observation errors, i.e., These are two standard least square problems. The estimation for αj and βj are in Equation (7). ε ⎧ ⎡ 2 ⎤⎫ ⎡ x ⎤ ⎪⎡0⎤ σ x 0 ⎪ ˆ T −1 T ε = , ε ~ N⎨ , ⎢ ⎥⎬ (4) AWWWX= () ⎢ε ⎥ ⎢ ⎥ 2 (7) y ⎪⎣0⎦ ⎢ 0 σ y ⎥⎪ T −1 T ⎣ ⎦ ⎩ ⎣ ⎦⎭ BWWWYˆ = () the equations constructed from the GCPs by (3) could be The corresponding variance components estimation (Ou, 1989) separated into two sets of equations as shown in (5) below, are in Equation (8). X= WA + ε x 2 1 ˆ T ˆ ) 5 σˆ (x =n− r (WA− X)( WA − X ) Y= WB + ε (8) y ˆ 2 1 ˆ T ˆ σ y =n− r (WB − Y)( WB − Y ) where where r = rank(W). Thus, the transformation functions f and g are determined. The total variance could be estimated by: 2 2 2 σˆt= σ ˆ x + σ ˆ y ) 9 ( 1074 The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part B4. Beijing 2008 All products (except RAW) are georeferenced, but not Sometimes we also need to determine the inverse geocorrected. Since these products are not geographically transformation of Equation (1) from the GCPs set, which corrected, the geographic metadata included in GeoTIFF is transforms the coordinate from (xi, yi) to (ui, vi). limited to the four corners tying image location to geographic location. GeoTIFF images are generated in TIFF strip format. Multi-polar images will be generated as separate GeoTIFF = −1 yxfu α′)/,( image files.
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