Unsupervised Learning Applied to Sentinel-1 for Shallow Waters Exploration in Galicia (Spain)

The Horizon Europe S34I project aims to enhance exploration methods to secure critical raw materials (CRM) and location management through innovative methods to process Earth Observation data. Placer detection using Copernicus optical data was recently assessed on the Iberian Peninsula Atlantic coast, but radar data potential is still unknown. This search evaluates the contributions from Sentinel-1 data for placer exploration through textural analysis and unsupervised learning with K-means. Different numbers of clusters and iterations were tested, and different attempts were created using several input features for unsupervised classification. RGB compositions were tested to further explore the contribution of the textural indices. The results show the potential of Sentinel-1 data and unsupervised learning in identifying distinct classes in the foreshore and intertidal zones. The Homogeneity textural index allowed for the discrimination of different classes within the Spanish rias while onshore it highlighted geological contacts and fault zones. This study showcases radar data's potential for placer exploration, paving the path for new future applications.