DOI QR코드

DOI QR Code

고해상도 카메라와의 동시 운영을 통한 드론 다분광카메라의 외부표정 및 영상 위치 정밀도 개선 연구

Improving Precision of the Exterior Orientation and the Pixel Position of a Multispectral Camera onboard a Drone through the Simultaneous Utilization of a High Resolution Camera

  • Baek, Seungil (Dept. of Civil and Environmental Engineering, Pusan National University) ;
  • Byun, Minsu (Dept. of Civil and Environmental Engineering, Pusan National University) ;
  • Kim, Wonkook (Dept. of Civil and Environmental Engineering, Pusan National University)
  • 투고 : 2021.12.03
  • 심사 : 2021.12.21
  • 발행 : 2021.12.31

초록

최근 농업, 산림관리, 해안환경 모니터링 등 다양한 분야에서 다분광 카메라의 활용, 특히 드론에 탑재되어 활용되는 사례가 증대되고 있다. 산출되는 다분광 영상은 위치정보를 위해 주로 드론에 탑재된 GPS (Global Positioning System)나 IMU (Inertial Measurement Unit) 센서를 이용해 지리참조(georeferencing)되는데, 보다 높은 정확도를 위해서는 직접 측량한 지상 기준점을 이용하기도 한다. 하지만, 직접 측량에 드는 비용 및 시간으로 인해 또는 직접 접근이 어려운 지역에 대해서는 지상 참조값을 활용하지 않고 지리참조를 수행해야하는 경우가 자주 발생하게 된다. 본 연구는 지상기준점이 가용하지 않은 경우에 다분광카메라로부터의 영상의 지리참조 정밀도를 향상시키기 위해 같이 탑재된 고해상도 RGB카메라의 영상을 활용하는 방안에 대하여 연구한다. 드론 영상은 우선 번들조정을 통해 카메라의 외부표정 요소를 추정하였고, 이를 지상 기준점을 이용한 경우의 외부표정 및 위치결과와 비교하였다. 실험결과, 고해상도 영상을 포함하여 번들조정을 하게 될 경우, 다분광 카메라 영상을 단독으로 활용할 때보다, 다분광 카메라 영상의 지리참조 오차가 비약적으로 감소하였음을 확인하였다. 추가로 한 지상 지점에서 드론으로의 방향각을 추정할 때의 오차를 분석한 결과, 마찬가지로 고해상도 RGB영상을 포함하여 번들조정하게 되면 기존의 방향각 오차가 한 단위이상 감소하는 것으로 나타났다.

Recently, multispectral cameras are being actively utilized in various application fields such as agriculture, forest management, coastal environment monitoring, and so on, particularly onboard UAV's. Resultant multispectral images are typically georeferenced primarily based on the onboard GPS (Global Positioning System) and IMU (Inertial Measurement Unit)or accurate positional information of the pixels, or could be integrated with ground control points that are directly measured on the ground. However, due to the high cost of establishing GCP's prior to the georeferencing or for inaccessible areas, it is often required to derive the positions without such reference information. This study aims to provide a means to improve the georeferencing performance of a multispectral camera images without involving such ground reference points, but instead with the simultaneously onboard high resolution RGB camera. The exterior orientation parameters of the drone camera are first estimated through the bundle adjustment, and compared with the reference values derived with the GCP's. The results showed that the incorporation of the images from a high resolution RGB camera greatly improved both the exterior orientation estimation and the georeferencing of the multispectral camera. Additionally, an evaluation performed on the direction estimation from a ground point to the sensor showed that inclusion of RGB images can reduce the angle errors more by one order.

키워드

과제정보

본 연구는 산림청(한국임업진흥원) 산림과학기술 연구개발사업 (FTIS2020179A00-2022-BB01)의 지원에 의하여 이루어진 것입니다.

참고문헌

  1. Agisoft, L. (2018), Agisoft PhotoScan user manual: professional edition. Manual 1.4, Agisoft LLC., St Petersburg, Russia
  2. Assmann, J.J., Kerby, J.T., Cunliffe, A.M., and Myers-Smith, I.H. (2019), Vegetation monitoring using multispectral Sensors-Best practices and lessons learned from high latitudes. Journal of Unmanned Vehicle Systems. Vol. 7, No. 1, pp. 54-75. https://doi.org/10.1139/juvs-2018-0018
  3. Baek, J.Y., Jo, Y.H., Kim, W., Lee, J.S., Jung, D., Kim, D.W., and Nam, J. (2019), A New Algorithm to Estimate Chlorophyll-A Concentrations in Turbid Yellow Sea Water Using a Multispectral Sensor in a Low-Altitude Remote Sensing System. Remote Sensing, Vol. 11, No. 19, pp. 2257. https://doi.org/10.3390/rs11192257
  4. Chakhvashvili, E., Siegmann, B., Bendig, J., and Rascher, U. (2021), Comparison of Reflectance Calibration Workflows for a UAV-Mounted Multi-Camera Array System, 2021 IEEE International Geoscience and Remote Sensing Symposium (IGARSS). 12-16 July, Brussels, Belgium, pp. 8225-8228.
  5. Chen, P.C., Chiang, Y.C., and Weng, P.Y. (2020), Imaging using unmanned aerial vehicles for agriculture land use classification. Agriculture, Vol.10, No. 9 , pp. 416. https://doi.org/10.3390/agriculture10090416
  6. Dash, J.P., Pearse, G., Watt, M., 2018. UAV Multispectral Imagery Can Complement Satellite Data for Monitoring Forest Health. Remote Sensing, Vol. 10, No. 8, pp. 1216. https://doi.org/10.3390/rs10081216
  7. Dash, J.P., Watt, M.S., Pearse, G.D., Heaphy, M., and Dungey, H.S. (2017), Assessing very high resolution UAV imagery for monitoring forest health during a simulated disease outbreak. ISPRS Journal of Photogrammetry and Remote Sensing, Vol. 131, pp. 1-14. https://doi.org/10.1016/j.isprsjprs.2017.07.007
  8. de Oca, A.M., Arreola, L., Flores, A., Sanchez, J., and Flores, G. (2018), Low-cost multispectral imaging system for crop monitoring, 2018 International Conference on Unmanned Aircraft Systems (ICUAS), IEEE, 12-15 Jun, Dallas, TX, USA, pp. 443-451.
  9. Kim, W., Jung, S., Kim, K., Ryu, J.H., and Moon, Y. (2020), Mapping Red Tide Intensity Using Multispectral Camera on Unmanned Aerial Vehicle: A Case Study in Korean South Coast, 2020 IEEE International Geoscience and Remote Sensing Symposium(IGARSS), 26 September-2 October, Waikoloa, HI, USA, pp. 5612-5615.
  10. Luhmann, T., Fraser, C., and Maas, H.G. (2016), Sensor modelling and camera calibration for close-range photogrammetry. ISPRS Journal of Photogrammetry and Remote Sensing, Vol. 115, pp. 37-46. https://doi.org/10.1016/j.isprsjprs.2015.10.006
  11. Lum, C., Mackenzie, M., Shaw-Feather, C., Luker, E., and Dunbabin, M. (2016), Multispectral Imaging and Elevation Mapping from an Unmanned Aerial System for Precision Agriculture Applications, Proceedings of the 13th International Conference on Precision Agriculture, 31 Jul - 03 Aug, St. Louis, Missouri, USA, pp. 4-10
  12. Shin, D., Kim, D., Kim, S., Han, Y., and Nho, H. (2020), A Study on the Use of Drones for Disaster Damage Investigation in Mountainous Terrain. Korean Journal of Remote Sensing, Vol. 36, No. 5_4, pp. 1209-1220. (in Korean with English abstract) https://doi.org/10.7780/KJRS.2020.36.5.4.6
  13. Su, J., Liu, C., Coombes, M., Hu, X., Wang, C., Xu, X., Li, Q., Guo, L., and Chen, W.H. (2018), Wheat yellow rust monitoring by learning from multispectral UAV aerial imagery. Computers and Electronics in Agriculture Vol. 155, pp. 157-166. https://doi.org/10.1016/j.compag.2018.10.017
  14. Tomastik, J., Mokros, M., Surovy, P., Grznarova, A., and Merganic, J. (2019), UAV RTK/PPK Method-An Optimal Solution for Mapping Inaccessible Forested Areas?. Remote Sensing, Vol.11, No.6 , pp. 721. https://doi.org/10.3390/rs11060721