Despite the recent development in light detection and ranging (LIDAR) systems, discrepancies between strips in overlapping areas persist because of systematic errors. During the past decade, several methods have been developed for compensating for errors, such as checking the coincidence of conjugate features extracted from overlapping LIDAR strips or comparing interpolated range and intensity images. However, these approaches rely upon the ability to detect and extract suitable conjugate features within the overlap area and/or during the preprocessing of raw LIDAR data in, for example, interpolation or segmentation. Such procedures make the overall process complex and may impose limitations on the development of an automated method. Furthermore, some of the preprocessing techniques, such as raster interpolation, may induce errors in raw LIDAR data when dealing with large-scale coverage over an urban area. This paper therefore presents an automated approach, working with raw LIDAR data without the restrictions associated with using conjugate features and without any preprocessing. We present an approach using changes in local height variations that occur within the overlap area between two neighboring strips. In this case, local height variations of the LIDAR data in the overlap area increase if there are discrepancies. This scheme can be helpful in determining an appropriate transformation for the adjustment of discrepancies between neighboring LIDAR strips in a way that minimizes the local height variation. A contour tree (CT) was used to represent the local height variations and to find an appropriate initial transformaunction. The iterative closest point (ICP) procedure was then applied to refine the function parameters. Following transformation, LIDAR strips were registered with each other, and the discrepancies were measured again to determine whether they had been resolved. The statistical evaluation of the results revealed that the discrepancies were significantly reduced.
|Number of pages||20|
|Journal||GIScience and Remote Sensing|
|Publication status||Published - 2010 Jan 1|
Bibliographical noteFunding Information:
We would like to recognize the Technology Institute for Development–LATEC– UFPR for supplying the LIDAR data. This research was supported by a grant (07KLSGC04) from Cutting-edge Urban Development—Korean Land Spatialization Research Project funded by the Ministry of Construction and Transportation of the Korean Government.
All Science Journal Classification (ASJC) codes
- Earth and Planetary Sciences(all)