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Performance assessment of image processing systems may be carried out using large volumes of data with known ground truth. Unfortunately such data, collected in sensor trials, can be challenging to source for many problems of interest. In particular, trials collection may require the acquisition of imagery in a range of scenario settings, imaging geometries and environmental conditions. An alternative to trials data collection uses synthetically generated imagery of objects and environments configured into realistic scenarios. For performance assessment of image processing chains, large volumes of synthetic imagery may be required in order to characterise individual algorithmic steps or for complete system assessment. In order to generate sufficiently large volumes for such characterisations the simulation approach must also be fast to execute. This paper presents a process for the generation of simulated Synthetic Aperture Radar (SAR) imagery which is fit-for-purpose for the task of algorithm and systems performance assessment of image processing for Automatic Target Detection, Recognition and Identification (ATDRI) tasks. The approach taken is based on the exploitation of computational geometry primitives. It uses a simplified imaging model and correctly treats both layover effects and shadowed regions on both the target object and within the background region. For speed and simplicity the simulation process synthesises single bounce reflections only. This means that the simulation is effective only up to the intermediate resolutions which are typically used for ATDRI applications. The input models are comprised of three-dimensional triangulations representing the geometric structure of the scene content, with each triangle having a parameterised scattering response based on distributional models often used for SAR imagery. The synthesis process generates a collection of two-dimensional arrays of distributional parameters of the same size as the image to be produced. It is straightforward to use these to generate representations of, for example, mean scattering response, or realistic-looking simulated SAR images with speckle ‘noise’. Results are presented for different scene content and sensor configurations, including target aspect and sensor depression angles.
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