An automated workflow for observing track data in 3-dimensional geo-accurate environments

Recent developments in computing capabilities and persistent surveillance systems have enabled advanced analytics and visualization of image data. Using our existing capabilities, this work focuses on developing a unified approach to address the task of visualizing track data in 3-dimensional environments. Our current structure from motion (SfM) workflow is reviewed to highlight our point cloud generation methodology, which offers the option to use available sensor telemetry to improve performance. To this point, an algorithm outline for navigation-guided feature matching and geo-rectification in the absence of ground control points (GCPs) is included in our discussion. We then provide a brief overview of our onboard processing suite, which includes real-time mosaic generation, image stabilization, and feature tracking. Exploitation of geometry refinements, inherent to the SfM workflow, is then discussed in the context of projecting track data into the point cloud environment for advanced visualization. Results using the new Exelis airborne collection system, Corvus Eye, are provided to discuss conclusions and areas for future work.