Lidar tomography is a method that constructs high-resolution images of objects from multiple range projections along different projection axes. This approach is one way to overcome traditional limitations in remote sensing with focal imaging such as diffraction, optical aberrations, and air turbulence. We have shown previously through detailed modelling and simulation that lidar tomography can generate resolved imagery of objects from a moving platform if sufficient diversity of view angles and appropriate geolocation accuracy requirements can be met. Here we show that the geolocation accuracy requirements can be met through a data-driven approach that does not require accurate knowledge of the platform’s position relative to the object being imaged. This alleviates a significant technical burden of motion tracking and opens the way for a more practical implementation of the lidar tomography technique for remote sensing and imaging.
Most lidar approaches configured for long range applications rely on large aperture telescopes or else aperture synthesis to resolve objects. Here we describe a different technique that draws its inspiration from computed tomography (CT) scan technology. This method, called lidar tomography, relies on narrow laser pulses to generate a series of highly-range resolved measurements from different look angles as the lidar platform moves around the object. We compare the convolutional backprojection technique for image reconstruction to a model based iterative algorithm to reconstruct images of various objects in a lab. We show through modelling and simulation that the lidar tomography approach can generate <1-inch resolved imagery from an airborne platform if sufficient angular diversity and appropriate geolocation accuracy requirements can be met.
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