Semi-Automatization of Support Vector Machines to Map Lithium (Li) Bearing Pegmatites

• Type: Article in International Scientific Journal
• Date: 2020

Authors

Cardoso Fernandes, J (Author) Other; Ana Teodoro (Author) FCUP; Alexandre Lima (Author) FCUP; Roda Robles, E (Author) Other

Journal

Title: Remote Sensing

Vol. 12

Final page: 2319

ISSN: 2072-4292

Publisher: MDPI

Indexing

ISI Web of Knowledge - 62 Citations

Scopus - 74 Citations

Other information

Authenticus ID: P-00S-EBY

DOI: 10.3390/rs12142319

Abstract (EN): Machine learning (ML) algorithms have shown great performance in geological remote sensing applications. The study area of this work was the Fregeneda-Almendra region (Spain-Portugal) where the support vector machine (SVM) was employed. Lithium (Li)-pegmatite exploration using satellite data presents some challenges since pegmatites are, by nature, small, narrow bodies. Consequently, the following objectives were defined: (i) train several SVM's on Sentinel-2 images with different parameters to find the optimal model; (ii) assess the impact of imbalanced data; (iii) develop a successful methodological approach to delineate target areas for Li-exploration. Parameter optimization and model evaluation was accomplished by a two-staged grid-search with cross-validation. Several new methodological advances were proposed, including a region of interest (ROI)-based splitting strategy to create the training and test subsets, a semi-automatization of the classification process, and the application of a more innovative and adequate metric score to choose the best model. The proposed methodology obtained good results, identifying known Li-pegmatite occurrences as well as other target areas for Li-exploration. Also, the results showed that the class imbalance had a negative impact on the SVM performance since known Li-pegmatite occurrences were not identified. The potentials and limitations of the methodology proposed are highlighted and its applicability to other case studies is discussed.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 22