Proceedings Article | 13 November 2024
KEYWORDS: Cooccurrence matrices, Image processing, Feature extraction, Minerals, Synthetic aperture radar, Hough transforms, Satellites, Raw materials, Radiometric corrections, Polarization
The Rias Baixas region in southwestern Galicia, Spain, is geologically significant for its heavy-mineral placer deposits, which include critical raw materials such as Ti, Sn, Li, Rare Earth Element (REE), Au, Nb, and Ta. These materials are strategically important to the European Union. Geologically, the region is part of the Galicia Trás-os-Montes Zone, characterised by a complex fracture system. Structural mapping of the Ria de Vigo was conducted using Sentinel-1 Synthetic Aperture Radar (SAR) data, processed on the cloud-based Google Earth Engine (GEE) and the European Space Agency (ESA)'s Sentinel Application Platform (SNAP). The Sentinel-1 data underwent preprocessing, followed by the application of the Grey Level Co-Occurrence Matrix (GLCM) on both SNAP and GEE. The computed co-occurrence indices were Contrast, Angular Second Moment (ASM), and Correlation (CORR). The GEE indices were compared with those generated in SNAP. The Contrast index yielded the best results, while ASM and CORR images were noisy and obscured geological lineaments, particularly compared to the SNAP-generated indices. Automatic lineament extraction was performed using the VV band and both Contrast bands, with the SNAP-derived Contrast band imported into GEE. The Canny Algorithm and Hough Transform were employed for automatic extraction in GEE. Finally, the length and direction of the extracted lineaments were analysed. Results showed significant differences: lineaments from the VV band and SNAP-generated Contrast band closely matched known literature, while those from the GEE-generated Contrast band were unsatisfactory, exhibiting curvilinear lineaments. This disparity is likely due to the way GLCM was employed in GEE, indicating the need for it to be explored further in future studies.
