Go to:
Logótipo
Comuta visibilidade da coluna esquerda
Você está em: Start > Publications > View > Machine Learning Algorithms for Automatic Lithological Mapping Using Remote Sensing Data: A Case Study from Souk Arbaa Sahel, Sidi Ifni Inlier, Western Anti-Atlas, Morocco
Publication

Publications

Machine Learning Algorithms for Automatic Lithological Mapping Using Remote Sensing Data: A Case Study from Souk Arbaa Sahel, Sidi Ifni Inlier, Western Anti-Atlas, Morocco

Title
Machine Learning Algorithms for Automatic Lithological Mapping Using Remote Sensing Data: A Case Study from Souk Arbaa Sahel, Sidi Ifni Inlier, Western Anti-Atlas, Morocco
Type
Article in International Scientific Journal
Year
2019
Authors
Bachri, I
(Author)
Other
The person does not belong to the institution. The person does not belong to the institution. The person does not belong to the institution. Without AUTHENTICUS Without ORCID
Hakdaoui, M
(Author)
Other
The person does not belong to the institution. The person does not belong to the institution. The person does not belong to the institution. Without AUTHENTICUS Without ORCID
Raji, M
(Author)
Other
The person does not belong to the institution. The person does not belong to the institution. The person does not belong to the institution. Without AUTHENTICUS Without ORCID
Ana Teodoro
(Author)
FCUP
View Personal Page You do not have permissions to view the institutional email. Search for Participant Publications View Authenticus page View ORCID page
Benbouziane, A
(Author)
Other
The person does not belong to the institution. The person does not belong to the institution. The person does not belong to the institution. Without AUTHENTICUS Without ORCID
Journal
Vol. 8
Final page: 248
Publisher: MDPI
Other information
Authenticus ID: P-00Q-PJV
Abstract (EN): Remote sensing data proved to be a valuable resource in a variety of earth science applications. Using high-dimensional data with advanced methods such as machine learning algorithms (MLAs), a sub-domain of artificial intelligence, enhances lithological mapping by spectral classification. Support vector machines (SVM) are one of the most popular MLAs with the ability to define non-linear decision boundaries in high-dimensional feature space by solving a quadratic optimization problem. This paper describes a supervised classification method considering SVM for lithological mapping in the region of Souk Arbaa Sahel belonging to the Sidi Ifni inlier, located in southern Morocco (Western Anti-Atlas). The aims of this study were (1) to refine the existing lithological map of this region, and (2) to evaluate and study the performance of the SVM approach by using combined spectral features of Landsat 8 OLI with digital elevation model (DEM) geomorphometric attributes of ALOS/PALSAR data. We performed an SVM classification method to allow the joint use of geomorphometric features and multispectral data of Landsat 8 OLI. The results indicated an overall classification accuracy of 85%. From the results obtained, we can conclude that the classification approach produced an image containing lithological units which easily identified formations such as silt, alluvium, limestone, dolomite, conglomerate, sandstone, rhyolite, andesite, granodiorite, quartzite, lutite, and ignimbrite, coinciding with those already existing on the published geological map. This result confirms the ability of SVM as a supervised learning algorithm for lithological mapping purposes.
Language: English
Type (Professor's evaluation): Scientific
No. of pages: 20
Documents
We could not find any documents associated to the publication.
Related Publications

Of the same journal

Procedural Point Cloud Modelling in Scan-to-BIM and Scan-vs-BIM Applications: A Review (2023)
Another Publication in an International Scientific Journal
Abreu, N; Pinto, A; Aníbal Castilho Coimbra de Matos; Pires, M
Radio Astronomy Demonstrator: Assessment of the Appropriate Sites through a GIS Open Source Application (2016)
Article in International Scientific Journal
Lia Duarte; Ana Teodoro; Maia, D; Barbosa, D
Local Segregation of Realised Niches in Lizards (2020)
Article in International Scientific Journal
sillero, n; Argana, E; Matos, C; Franch, M; Kaliontzopoulou, A; carretero, ma
Hybrid Machine Learning Approach for Gully Erosion Mapping Susceptibility at a Watershed Scale (2022)
Article in International Scientific Journal
Hitouri, S; Varasano, A; Mohajane, M; Ijlil, S; Essahlaoui, N; Ali, SA; Essahlaoui, A; Pham, QB; Waleed, M; Palateerdham, SK; Ana Teodoro

See all (14)

Recommend this page Top
Copyright 1996-2025 © Faculdade de Direito da Universidade do Porto  I Terms and Conditions  I Acessibility  I Index A-Z
Page created on: 2025-07-18 at 20:13:48 | Privacy Policy | Personal Data Protection Policy | Whistleblowing