Virtual Confocal Microscopy

There is a need for persistent-surveillance assets to capture high-resolution, three-dimensional data for use in assisted target recognizing systems. Passive electro-optic imaging systems are presently limited by their ability to provide only 2-D measurements. We describe a methodology and system that uses existing technology to obtain 3-D information from disparate 2-D observations. This data can then be used to locate and classify objects under obscurations and noise. We propose a novel methodology for 3-D object reconstruction through use of established confocal microscopy techniques. A moving airborne sensing platform captures a sequence of geo-referenced, electro-optic images. Confocal processing of this data can synthesize a large virtual lens with an extremely sharp (small) depth of focus, thus yielding a highly discriminating 3-D data collection capability based on 2-D imagery. This allows existing assets to be used to obtain high-quality 3-D data (due to the fine z-resolution). This paper presents a stochastic algorithm for reconstruction of a 3-D target from a sequence of affine projections. We iteratively gather 2-D images over a known path, detect target edges, and aggregate the edges in 3-D space. In the final step, an expectation is computed resulting in an estimate of the target structure.