Potential of DESIS and PRISMA hyperspectral remote sensing data in rock classification and mineral identification:a case study for Banswara in Rajasthan, India

PRISMA Hyperspectral Remote Sensing Data for Mapping Alteration Minerals in Sar-e-Châh-e-Shur Region, Birjand, Iran

“…Accordingly, alteration mineral maps derived from PRISMA are specifically inclusive compared to previous studies using ASTER multispectral data in the study region [71][72][73][74]. In addition, some previous studies accentuated that PRISMA datasets contain great capability providing valuable insights for hydrothermal alteration mapping of ore mineralizations in metallogenic provinces around the world [2,[7][8][9][14][15][16][17][18][19][20][21][22][23][24][25]75,76]. The escalating challenges associated with mineral exploration underscore the critical need for innovative strategies, particularly in the identification of high-potential zones and subsequent campaigns.…”

“…The Principal Component Analysis (PCA), Minimum Noise Fraction (MNF), Independent Component Analysis (ICA), Adaptive Coherence Estimator (ACE), Random Forest (RF), XGboost (XGB), Support Vector Machine (SVM) and many other machine-learningbased classification algorithms were used for processing PRISMA data in mapping alteration minerals, identifying economic mineralization prospects, delineating dolomitization, obtaining high-quality reflectance estimations and achieving high-accuracy lithological mapping [2,[7][8][9][10][14][15][16][17][18][19]. Additionally, novel approaches and algorithms such as the spectral hourglass, iterative informed spectral unmixing technique, fuzzy logic approach, GIS-based algorithm, and informed linear mixing model were implemented to PRISMA data to determine the potential of the dataset for automated alteration mineral identification in diverse environments [20][21][22][23][24][25].…”