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Automated target recognition has benefited from cross- fertilization of development in related subdisciplines of image processing such as medical imaging. For example, the application of computerized tomography to synthetic aperture radar (SAR) imaging has produced 3-D reconstructions of ground targets on an experimental basis. In practice, by acquiring multiple views of a target (also called multi-look imaging -- MLI) that are subsequently merged mathematically, one can obtain reasonable approximations to higher-dimensional reconstructions of a target of interest. For example, multiple two-dimensional airborne images of ground objects can be merged via the Fourier transform (FT) to obtain one or more approximate three-dimensional object reconstructions. Additional methods of 3D model construction (e.g., from affine structure) present advantages of computational efficiency, but are sensitive to positioning errors. In this series of papers, analysis of MLI is presented that applies to various scenarios of nadir, near-nadir, or off-nadir viewing with a small or large number of narrow-or wide-angle views. A model of imaging through cover describes the visibility of a given target under various viewing conditions. The model can be perturbed to obtain theoretical and simulated predictions of target reconstruction error due to (1) geometric projection error, (2) focal-plane quantization error and camera noise, (3) possible sensor platform errors, and (4) coverage of looks. In this paper, an imaging model is presented that can facilitate prediction of limiting sensor geometry and view redundancy under various imaging constraints (e.g., target and cover geometry, available range of look angles, etc.). Study notation is a subset of image algebra, a rigorous, concise, computationally complete notation that unifies linear and nonlinear mathematics in the image domain. Image algebra was developed at University of Florida over the past decade under the sponsorship of DARPA and the U.S. Air Force, and has been implemented on numerous sequential workstations and parallel processors. Hence, our algorithms are rigorous and widely portable.
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