Spectral pattern classification in lidar data for rock identification in outcrops

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Abstract

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.

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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

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