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.
Several studies on remote sensing and mineral exploration have been developed or improved in the last decade. However, the low spatial resolution of satellites is a recurring problem in many cases where the mineral or rock is much smaller than the pixel size of the satellite images. This phenomenon, called sub-pixel occurrence, generates an extremely mixed pixel that difficult the performance of conventional classification algorithms. Satellites with high spatial resolution, such as the Worldview-3 (2 m Ground Sample Distance), have become an essential tool for mineral exploration studies. In addition to its high spatial resolution, the Worldview-3 has 16 bands, eight in the Visible and Near-infrared (VNIR) and eight in the Shortwave Infrared (SWIR) region, which further increases its potential for mineral exploration. This study applies a spectral unmixing-based method, using the Spectral Hourglass Wizard Workflow (SHW), to extract and select pegmatites endmembers from the WorldView-3 images. Further, these endmembers were used for a subpixel classification, performed through Mixture Tuned Matched Filtering (MTMF), to map possible pegmatite outcrops in the Tysfjord pegmatite field, Norway. After classification a high Digital Terrain Model (DTM) hillshade acquired from LiDAR technology and geological data were used to eliminate false positives. The subpixel classification results were compared with geological data and 17 points of interest for pegmatite exploration were selected for further field validation. This work shows the potential of the Worldview-3 high-resolution images processed with a spectral unmixing-based method for mineral exploration in an area of subpixel occurrence. The results are encouraging and show great value for the scientific field of pegmatite exploration.
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