Paper
19 October 2023 Automated lineament extraction applied to high-resolution imagery Worldview-3 and LiDAR data for pegmatite mineral exploration
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Abstract
Lineament extraction is a commonly used technique in mineral exploration to identify geological structures such as fault scarps, joints, and folds. However, the accuracy of this technique can be limited by factors such as the low spatial resolution of the data. This study aims to address these limitations by exploring the potential of high spatial resolution data for extracting linear structures in Tysfjord, northern Norway. Two types of high-resolution data were utilized for lineament extraction: (i) WorldView-3 (WV3) satellite orbital imagery with a ground sample distance (GSD) of two meters, and (ii) Light Detection and Ranging (LiDAR) point cloud data, with a GSD of one meter. The LiDAR point cloud was utilized to generate a Digital Terrain Model (DTM), and automated lineament extraction was performed on both WV3 and LiDAR data using the lineament algorithm (LINE) available in PCI Geomatics software. A comparison was conducted using Sentinel two images to analyze the impact of utilizing high-resolution images on the final results. The outcomes illustrate that high-resolution images hold substantial potential for extracting lineaments and can aid in identifying mineral deposits and neotectonic activity. In the future, these findings could be integrated with other remote sensing methods to enhance the capabilities of remote sensing for mineral exploration.
(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.
D. Santos, A. Mendes, A. Azzalini, J. Cardoso-Fernandes, A. Lima, and A. C. Teodoro "Automated lineament extraction applied to high-resolution imagery Worldview-3 and LiDAR data for pegmatite mineral exploration", Proc. SPIE 12734, Earth Resources and Environmental Remote Sensing/GIS Applications XIV, 1273414 (19 October 2023); https://doi.org/10.1117/12.2679367
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KEYWORDS
Spatial resolution

Minerals

Image processing

LIDAR

Mining

Data modeling

Remote sensing

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