Abstract
The use of unmanned air vehicle (UAV) images acquired by a non-metric digital camera to establish an image network is difficult in cases without accurate camera model parameters. Although an image network can be generated by continuously calculating camera model parameters during data processing as an incremental structure from motion (SfM) methods, the process is time consuming. In this study, low-cost global position system (GPS) information is employed in image network generation to decrease computational expenses. Each image is considered as reference, and its neighbor images are determined based on GPS coordinates during processing. The reference image and its neighbor images constitute an image group, which is used to generate a free network through image matching and relative orientation. Data are then transformed from the free network coordinate system of each group into the GPS coordinate system by using the GPS coordinates of each image. After the exterior elements of each image are determined in the GPS coordinate system, the initial image network is established. Finally, self-calibration bundle adjustment constrained by GPS coordinates is conducted to refine the image network. The proposed method is validated on three fields. Results confirm that the method can achieve good image network when accurate camera model parameters are unavailable.
Highlights
Unmanned air vehicles (UAV) are commonly used for terrain measurements because of their low costs, mobilization, flexibility, and high spatial resolution (Gonçalves and Henriques, 2015; Hui et al, 1998; Uysal et al, 2015)
structure from motion (SfM) is driven by enabling automated model reconstruction from imagery with few limitations in types of digital cameras and viewing angles (Uysal et al, 2015; Westoby et al, 2012).With the advantages of the modern digital camera technology, nonmetric cameras, such as digital single-lens reflex cameras (DSLRs), are widely used, on the platform of UAVs
The coordinates are transformed into the Euclidean coordinate system through Gauss Projection before image network generation because the original global position system (GPS) coordinates are observed in the WGS-84 coordinate system when output from the GPS receiver
Summary
Unmanned air vehicles (UAV) are commonly used for terrain measurements because of their low costs, mobilization, flexibility, and high spatial resolution (Gonçalves and Henriques, 2015; Hui et al, 1998; Uysal et al, 2015). Some UAV photogrammetry systems only employ low-cost GPS receivers to decrease device weight and cost but rarely use the inertial measurement unit because it is expensive. These systems can only obtain the location of images with low precision. SfM is driven by enabling automated model reconstruction from imagery with few limitations in types of digital cameras and viewing angles (Uysal et al, 2015; Westoby et al, 2012).With the advantages of the modern digital camera technology, nonmetric cameras, such as digital single-lens reflex cameras (DSLRs), are widely used, on the platform of UAVs. non-metric cameras have significant radial distortions, and camera parameters are unstable. Non-metric digital cameras applied for measurements may not be calibrated
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