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001-es BibID:	BIBFORM122872
Első szerző:	Shebl, Ali (geológus)
Cím:	PRISMA vs. Landsat 9 in lithological mapping ? a K-fold Cross-Validation implementation with Random Forest / Ali Shebl, Dávid Abriha, Maher Dawoud, Mosaad Ali Hussein Ali, Arpád Csámer
Dátum:	2024
ISSN:	1110-9823 2090-2476
Megjegyzések:	The selection of an optimal dataset is crucial for successful remote sensing analysis. The PRISMA hyperspectral sensor (with 240 spectral bands) and Landsat OLI-2 (boasting high dynamic resolution) offer robust data for various remote sensing applications, anticipating their increased demand in the coming years. However, despite their potential, we have not identified a rigorous evaluation of both datasets in geological applications utilizing Machine Learning Algorithms. Consequently, we conduct a comprehensive analysis using Random Forest, a widely-recommended machine learning algorithm, and employ K-fold cross-validation (with K = 2, 5, 10) with grid-search hyperparameter tuning for enhanced performance. Toward this aim, diverse image-processing ap- proaches, including Principal Component Analysis (PCA), Minimum Noise Fraction (MNF), and Independent Component Analysis (ICA), were applied to enhance feature selection and extraction. Subsequently, to ensure better performance of the RF algorithm, this study utilized well-distributed points instead of polygons to represent each target, thereby mitigating the effects of spatial autocorrelation. Our results reveal dataset- hyperparameter dependencies, with PRISMA mainly influenced by max_depth and Landsat 9 by max_features. Employing grid-search optimally balances dataset characteristics and data splitting (folds), generating accurate lithological maps across all K values. Notably, a significant hyperparameter shift at K = 10 produces the best lithological maps. Fieldwork and petrographic investigations validate the lithological maps, indicating PRISMA's slight superiority over Landsat OLI-2. Despite this, given the dataset nature and band count difference, we still advocate Landsat 9 as a potent multispectral input for future applications due to its superior radiometric resolution.
Tárgyszavak:	Természettudományok Földtudományok idegen nyelvű folyóiratközlemény külföldi lapban folyóiratcikk PRISMA Landsat 9 Random forest Geological mapping
Megjelenés:	Egyptian Journal of Remote Sensing and Space Science. - 27 : 3 (2024), p. 577-596. -
További szerzők:	Abriha Dávid (1995-) (geográfus) Dawoud, Maher Ali Hussein Ali, Mosaad Csámer Árpád (1976-) (geológus)
Pályázati támogatás:	K138079 NKFIH
Internet cím:	Szerző által megadott URL DOI Intézményi repozitóriumban (DEA) tárolt változat
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001-es BibID:	BIBFORM116157
035-os BibID:	(cikkazonosító)105652 (WoS)001078595200001 (Scopus)85171617945
Első szerző:	Shebl, Ali (geológus)
Cím:	PRISMA hyperspectral data for lithological mapping in the Egyptian Eastern Desert : Evaluating the support vector machine, random forest, and XG boost machine learning algorithms / Ali Shebl, David Abriha, Amr S. Fahil, Hanna A. El-Dokouny, Abdelmajeed A. Elrasheed, Arpád Csámer
Dátum:	2023
ISSN:	0169-1368
Megjegyzések:	In essence, targeting mineralization necessitates exact structural delineation and thorough lithological mapping. The latter is still a challenge for geologists and its lack hinders meticulous exploration for various mineralizations. Here we show for the first time over a case study from Arabian Nubian Shield (ANS), the application of hyperspectral PRISMA (PRecursore IperSpettrale della Missione Applicativa) data for objective lithological mapping using the well-known Random Forest (RF), XGboost (XGB), and Support Vector Machine (SVM) algorithms. Our results manifested the worthiness of PRISMA data in further lithological mapping, especially with SVM with a resultant accuracy depending mainly on the input data combination. Upon field verification, the current research reveals the usefulness of PRISMA and its preceding four principal components in delivering a detailed lithological map for the study area. Additionally, the eligibility of RF, XGB, and SVM was confirmed in delivering acceptable results. SVM exceeds XGB and RF in their overall accuracy (95 %, 92 %, and 90 % for SVM, XGB, and RF respectively). Our research strongly recommends blending the vantages of Machine Learning Algorithms' (MLAs) objectivity and the wealth of PRISMA spectral coverage for further precise lithological mapping before applicable mineral exploration programs in similar terrains.
Tárgyszavak:	Természettudományok Földtudományok idegen nyelvű folyóiratközlemény külföldi lapban folyóiratcikk PRISMA Lithological mapping Arabian Nubian Shield RF XGB SVM
Megjelenés:	Ore Geology Reviews. - 161 (2023), p. 1-16. -
További szerzők:	Abriha Dávid (1995-) (geográfus) Fahil, Amr S. El-Dokouny, Hanna A. Abdelmajeed, Adam Elrasheed Ali Csámer Árpád (1976-) (geológus)
Pályázati támogatás:	Stipendium Hungaricum scholarship Egyéb
Internet cím:	Szerző által megadott URL DOI Intézményi repozitóriumban (DEA) tárolt változat
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