Automatic Correction of DEM Surface Errors
- 1 Civil Engineering Department, Cal Poly University, Pomona, CA, USA
Abstract
The automation of extracting planner surfaces is a main field of research in digital photogrammetry. These surfaces are essential to generate three dimensional GIS databases. Surfaces are usually determined from either DEMs or images. Each dataset provides a different type of information. Thus, the combination of the two datasets should enhance the surface reconstruction process. This paper presents a new technique for generating 3D surfaces by combining both correlation-based DEMs and aerial images. The process starts by discriminating DEM points that represent planner surfaces using local statistics of neighboring elevations and intensities and point elevations. A segmented orthophoto is then used to group these points into different regions. The elevations of the points in each region are fed into a least squares adjustment model to compute the best-fit planner surface parameters. Refinement of surface borders is then performed using a region growing algorithm. The RMSE for five test sites showed a spatial accuracy of 5 - 8 cm.
- Rottensteiner, F. (1998) Object Reconstruction in a Bundle Block Environment. International Archives of Photogrammetry and Remote Sensing, Columbus, Ohio, USA, Vol. 32 (3/1), 6-10 July 1998, 177-183.
- Remondino, F. (2003) From Point Cloud to Surface: The Modeling and Visualization Problem. Proceedings of the International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, Tarasp-Vulpera, Switzerland, Vol. 34 (5/W10), 24-28 February 2003 (on CD-ROM).
- Mongus, D., Lukac, N. and Zalik, B. (2014) Ground and Building Extraction from LiDAR Data Based on Differential Morphological Profiles and Locally Fitted Surfaces. ISPRS Journal of Photogrammetry and Remote Sensing, 93, 145-156.
- Yang, B.S., Xu, W.X. and Yao, W. (2014) Extracting Buildings from Airborne Laser Scanning Point Clouds Using a Marked Point Process. GIScience & Remote Sensing, 51, 555-574. https://doi.org/10.1080/15481603.2014.950117
- Lari, Z. and Habib, A. (2014) An Adaptive Approach for the Segmentation and Extraction of Planar and Linear/Cylindrical Features from Laser Scanning Data. ISPRS Journal of Photogrammetry and Remote Sensing, 93, 192-212.
- Filin, S. (2002) Surface Clustering from Airborne Laser Scanning Data. Proceedings of the International Archives of Photogrammetry and Remote Sensing, Graz, Austria, Vol. 32(A), 9-13 September 2002, 119-124.
- Jochem, A., Hofle, B., Wichmann, V., Rutzinger, M. and Zipf, A. (2012) Area-Wide Roof Plane Segmentation in Airborne LiDAR Point Clouds. Computers, Environment and Urban Systems, 36, 54-64.
- Gilani, S.A.N., Awrangjeb, M. and Lu, G.J. (2016) An Automatic Building Extraction and Regularisation Technique Using LiDAR Point Cloud Data and Orthoimage. Remote Sensing, 8, 258. https://doi.org/10.3390/rs8030258
- Zhang, W.M., Wang, H.T., Chen, Y.M., Yan, K. and Chen, M. (2014) 3D Building Roof Modeling by Optimizing Primitive’s Parameters Using Constraints from LiDAR Data and Aerial Imagery. Remote Sensing, 6, 8107-8133. https://doi.org/10.3390/rs6098107
- Li, Y., Wu, H.Y., An, R., Xu, H.W., He, Q.S. and Xu, J. (2013) An Improved Building Boundary Extraction Algorithm Based on Fusion of Optical Imagery and LIDAR Data. Optik-International Journal for Light and Electron Optics, 124, 5357-5362.
- Hu, J., You, S. and Neumann, U. (2006) Integrating LIDAR, Aerial Image and Ground Images for Complete Urban Building Modeling. 3rd International Symposium on 3D Data Processing, Visualization and Transmission, Chapel Hill, 14-16 June 2006. https://doi.org/10.1109/3dpvt.2006.82