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

• Tipo: Artigo em Revista Científica Internacional
• Data: 2020

Autores

Cardoso Fernandes, J (Autor) Outra; Ana Teodoro (Autor) FCUP; Alexandre Lima (Autor) FCUP; Roda Robles, E (Autor) Outra

Revista

Título: Remote Sensing

Vol. 12

Página Final: 2319

ISSN: 2072-4292

Editora: MDPI

Indexação

ISI Web of Knowledge - 64 Citações

Scopus - 77 Citações

Outras Informações

ID Authenticus: 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.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 22