Remember Me
Or use your Academic/Social account:


Or use your Academic/Social account:


You have just completed your registration at OpenAire.

Before you can login to the site, you will need to activate your account. An e-mail will be sent to you with the proper instructions.


Please note that this site is currently undergoing Beta testing.
Any new content you create is not guaranteed to be present to the final version of the site upon release.

Thank you for your patience,
OpenAire Dev Team.

Close This Message


Verify Password:
Verify E-mail:
*All Fields Are Required.
Please Verify You Are Human:
fbtwitterlinkedinvimeoflicker grey 14rssslideshare1
J. L. Pérez-García; J. Delgado; J. Cardenal; C. Colomo; M. A. Ureña (2012)
Publisher: Copernicus Publications
Journal: The International Archives of the Photogrammetry
Languages: English
Types: Article
Subjects: TA1-2040, T, TA1501-1820, Applied optics. Photonics, Engineering (General). Civil engineering (General), Technology
At present, airborne laser scanner systems are one of the most frequent methods used to obtain digital terrain elevation models. While having the advantage of direct measurement on the object, the point cloud obtained has the need for classification of their points according to its belonging to the ground. This need for classification of raw data has led to appearance of multiple filters focused LiDAR classification information. According this approach, this paper presents a classification method that combines LiDAR data segmentation techniques and progressive densification to carry out the location of the points belonging to the ground. The proposed methodology is tested on several datasets with different terrain characteristics and data availability. In all case, we analyze the advantages and disadvantages that have been obtained compared with the individual techniques application and, in a special way, the benefits derived from the integration of both classification techniques. In order to provide a more comprehensive quality control of the classification process, the obtained results have been compared with the derived from a manual procedure, which is used as reference classification. The results are also compared with other automatic classification methodologies included in some commercial software packages, highly contrasted by users for LiDAR data treatment.
  • No references.
  • No related research data.
  • No similar publications.