Abstract
Abstract. One of the main novelties introduced by the contemporary spaceborne SAR sensors is the fine Ground Sample Distance (GSD). The GSD of 1 m, which is available now, is comparable to the resolution offered by optical sensors. It creates the conditions to bring closer SAR and optical data and directs research towards SAR processing methods of comparable accuracy with optical processing methods. This paper focuses on the capability offered by contemporary SAR sensors to identify fine man-made and physical features of earth's surface. 3D road edges are treated as Ground Control Information, in order to compute the projective transformation that relates the 3D features of the object space with their 2D projection in a SAR image. The computation of the projection is done with a novel ICP-based method that matches a network of 3D Free-form Linear Features of the object space with their 2D projections in the image space. The proposed method is tested for the georeference of a whole TerraSAR-X scene. Computed results are evaluated with independent Check Points. The quality of the results are superior to those computed with salient point based approaches.
Highlights
SAR is an all-weather, day and night sensor, offering information about the properties of the targets, their 3D geometry and their evolution through time
In the first test (Test A) the centerlines of 4 roads were used as Ground Control Linear Features (GCLFs)
The 3D GCLFs that were extracted from the map appear with cyan color, their 2D projection in the SAR image appear with magenta color, while the matched 3D GCLFs to the 2D free-form linear features (FFLFs), with the proposed method appear with black dashed line
Summary
SAR is an all-weather, day and night sensor, offering information about the properties of the targets, their 3D geometry and their evolution through time. Contemporary satellite SAR sensors are of high resolution, offering Ground Sampling Distance (GSD) as small as 1 meter. This novel characteristic, makes it possible to identify fine linear characteristics of earth's surface, such as roads (Eineder et al, 2009), which were not possible to identify in data sets collected by the previous generation of satellite SAR sensors. A novel method based on Iterative Closest Point (ICP) algorithm (Besl and McKay, 1992; Zhang, 1994) is used for accurate and robust heterogeneous free-form linear features (FFLFs) global matching. The results are compared to those computed through a salient points-based approach
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