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In recent years, photogrammetry has been widely used in many areas to createphotorealistic 3D virtual data representing the physical environment. Theinnovation of small unmanned aerial vehicles (sUAVs) has provided additionalhigh-resolution imaging capabilities with low cost for mapping a relativelylarge area of interest. These cutting-edge technologies have caught the US Armyand Navy's attention for the purpose of rapid 3D battlefield reconstruction,virtual training, and simulations. Our previous works have demonstrated theimportance of information extraction from the derived photogrammetric data tocreate semantic-rich virtual environments (Chen et al., 2019). For example, anincrease of simulation realism and fidelity was achieved by segmenting andreplacing photogrammetric trees with game-ready tree models. In this work, wefurther investigated the semantic information extraction problem and focused onthe ground material segmentation and object detection tasks. The maininnovation of this work was that we leveraged both the original 2D images andthe derived 3D photogrammetric data to overcome the challenges faced when usingeach individual data source. For ground material segmentation, we utilized anexisting convolutional neural network architecture (i.e., 3DMV) which wasoriginally designed for segmenting RGB-D sensed indoor data. We improved itsperformance for outdoor photogrammetric data by introducing a depth poolinglayer in the architecture to take into consideration the distance between thesource images and the reconstructed terrain model. To test the performance ofour improved 3DMV, a ground truth ground material database was created usingdata from the One World Terrain (OWT) data repository. Finally, a workflow forimporting the segmented ground materials into a virtual simulation scene wasintroduced, and visual results are reported in this paper.

Ground material classification for UAV-based photogrammetric 3D data A 2D-3D Hybrid Approach