OBJECTIVE: Develop and validate an automatic Ground Control Point (Auto-GCP) methodology for orthomosaic and provide advancements in object identification & mapping (KAZE algorithms2) for multiscale feature detection.
DESCRIPTION: Small UAS platforms have redefined squad level ISR collection processes providing an overmatch capability, aiding in soldier lethality and maneuver for both dismounted and mounted off-road mobility platforms. In addition to the challenges of operating small UAS in Artic environmental conditions, the post processing of imagery for orthomosaic and DSMs (photogrammetric surface modelling derived from Structure from Motion) is complex due to the poor availability of identifiable ground features and contrast from a highly reflective surface. Traditional photogrammetry methods rely on GCPs and distinct terrain features to align and process imagery. However, in Artic environments that are characterized by flat, snow-covered terrain, lacking these critical features, rendering traditional methods less accurate and often incalculable. Recent computer vision and remote sensing advancements in object identification and mapping (KAZE algorithms2) for multiscale feature detection in nonlinear scale space will enhance photogrammetric accuracies as well as provide a basis to derive feature matching algorithms for localization in the absence on GNSS.
The goal of this topic is to further define photogrammetric processes unique to Polar Environments from small UAS imagery collections3,4. Those processes will result in automated, near real time product generation that will aid in ground maneuverability, UAS maneuver (obstacle avoidance) and visual terrain referencing for operations in denied GNSS environments.
Speakers: Nathan Frantz
29 сен 2024
