Abstract
Unmanned Aerial Vehicles (UAVs) are an exciting new remote sensing tool capable of acquiring high resolution spatial data. Remote sensing with UAVs has the potential to provide imagery at an unprecedented spatial and temporal resolution. The small footprint of UAV imagery, however, makes it necessary to develop automated techniques to geometrically rectify and mosaic the imagery such that larger areas can be monitored. In this paper, we present a technique for geometric correction and mosaicking of UAV photography using feature matching and Structure from Motion (SfM) photogrammetric techniques. Images are processed to create three dimensional point clouds, initially in an arbitrary model space. The point clouds are transformed into a real-world coordinate system using either a direct georeferencing technique that uses estimated camera positions or via a Ground Control Point (GCP) technique that uses automatically identified GCPs within the point cloud. The point cloud is then used to generate a Digital Terrain Model (DTM) required for rectification of the images. Subsequent georeferenced images are then joined together to form a mosaic of the study area. The absolute spatial accuracy of the direct technique was found to be 65–120 cm whilst the GCP technique achieves an accuracy of approximately 10–15 cm.
Highlights
Unmanned Aerial Vehicles (UAVs) have primarily been used for military applications.More recently, the use of UAVs in the civilian domain as remote sensing tools presents new and exciting opportunities
Improvements in the availability of accurate and miniature Global Positioning Systems (GPS) and Inertial Measurement Units (IMUs), along with the availability of quality off-the-shelf consumer grade digital cameras and other miniature sensors have resulted in an increased use of civilian UAVs [1]
We describe a methodology for geometric image correction that uses new Computer Vision (CV) and Structure from Motion (SfM)
Summary
Unmanned Aerial Vehicles (UAVs) have primarily been used for military applications. GCPs, and we integrate the use of multiview stereopsis algorithms into the solution These techniques performed well but many are based on traditional photogrammetric software designed to process imagery collected from conventional platforms. Some of these techniques have some key disadvantages: they use existing underlying DTMs and base orthophotos, they rely on complex workflows to estimate camera EO parameters, and, in some cases, require human intervention to identify GCPs. In this study, we describe a methodology for geometric image correction that uses new CV and SfM algorithms that are more applicable to UAV photography. The automation and simplicity of our technique is ideally suited to UAV operations that generate large image data sets that require rectification and mosaicking prior to subsequent analysis
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