Volume XLI-B3
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B3, 31-37, 2016
https://doi.org/10.5194/isprs-archives-XLI-B3-31-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B3, 31-37, 2016
https://doi.org/10.5194/isprs-archives-XLI-B3-31-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.

  09 Jun 2016

09 Jun 2016

IMAGE NETWORK GENERATION OF UNCALIBRATED UAV IMAGES WITH LOW-COST GPS DATA

Shan Huang, Zuxun Zhang, Jianan He, and Tao Ke Shan Huang et al.
  • School of Remote Sensing and Information Engineering, Wuhan University, No.129 Luoyu Road, Wuhan, China

Keywords: UAV images, uncalibrated camera, Image Network Generation, GPS, Space Coordinate Transformation, Self-calibration, Bundle adjustment

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.