Laser scanner intensity calibration based on artificial neural networks

In this study, we propose a method to calibrate the laser pulse return intensity of a Terrestrial Laser Scanner (TLS) based on Artificial Neural Networks. The laser pulse return intensity has an important rule on rocks types' classification when using Digital Outcrops Models (DOM) and has been...

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Bibliographic Details
Published in2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS) pp. 1716 - 1719
Main Authors de Figueiredo, Rodrigo M., Veronez, Mauricio R., Tognoli, Francisco M. W., da Silva, Marcio R., Bordin, Fabiane, Gonzaga, Luiz, Koch, Ismael, Marson, Fernando P., Larocca, Ana P. C.
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.07.2017
Subjects
Artificial neural networks
Calibration
Imaging
Laser Intensity
Laser modes
Laser radar
LiDAR
Rocks
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Summary:In this study, we propose a method to calibrate the laser pulse return intensity of a Terrestrial Laser Scanner (TLS) based on Artificial Neural Networks. The laser pulse return intensity has an important rule on rocks types' classification when using Digital Outcrops Models (DOM) and has been the focus of much research by the geological community as it helps the geological interpretation in outcrops. In our experiment, we used a TLS Ilris 3D model with a wavelength of 1,535 nm. Our method has shown good efficiency for the calibration of the laser pulse return intensity, demonstrating a strong applicability for classification studies of rock types on Digital Outcrops Models.
ISSN:2153-7003
DOI:10.1109/IGARSS.2017.8127305