Spectral pattern classification in lidar data for rock identification in outcrops

Citations of this article
Mendeley users who have this article in their library.


The present study aimed to develop and implement a method for detection and classification of spectral signatures in point clouds obtained from terrestrial laser scanner in order to identify the presence of different rocks in outcrops and to generate a digital outcrop model. To achieve this objective, a software based on cluster analysis was created, named K-Clouds. This software was developed through a partnership between UNISINOS and the company V3D. This tool was designed to begin with an analysis and interpretation of a histogram from a point cloud of the outcrop and subsequently indication of a number of classes provided by the user, to process the intensity return values. This classified information can then be interpreted by geologists, to provide a better understanding and identification from the existing rocks in the outcrop. Beyond the detection of different rocks, this work was able to detect small changes in the physical-chemical characteristics of the rocks, as they were caused by weathering or compositional changes.




Campos Inocencio, L., Veronez, M. R., Wohnrath Tognoli, F. M., De Souza, M. K., Da Silva, R. M., Gonzaga, L., & Blum Silveira, C. L. (2014). Spectral pattern classification in lidar data for rock identification in outcrops. The Scientific World Journal, 2014. https://doi.org/10.1155/2014/539029

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free