Spectral Pattern Classification in Lidar Data for Rock Identification in Outcrops

Abstract EN

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.