Abstract
Hyperspectral remote sensing has evolved as an effective method for mineral identification and mapping, delivering comprehensive spectral data over a wide range of wavelengths. This study examines the usage of PRISMA hyperspectral data to accurately identify and map minerals in Southeast Tamil Nadu’s coastal regions. The raw PRISMA data is refined to reduce atmospheric interference and improve the signal-to-noise ratio (SNR). Advanced spectrum techniques are employed to identify and isolate mineral fingerprints. To increase accuracy in detecting various mineral assemblages, even in complex geological settings, a machine learning (ML) approach incorporating both unsupervised and supervised learning algorithm is utilized. Unsupervised learning methods such as K means clustering, principal component analysis (PCA), and vertex component analysis (VCA) are used for finding similar spectral signatures of mineral. Supervised learning methods such as convolutional neural networks (CNNs), random forests (RFs), logistic regression (LR), support vector machines (SVMs), K nearest neighbors (KNN), decision tree (DT), artificial neural networks (ANNs), linear discriminant analysis (LDA), and Naïve Bayes (NB) are evaluated, and different performance metrics are computed. The results show that CNN model outperforms other ML models interms of overall accuracy (OA) of 78% for mineral classification. Moreover, CNN model could successfully identify and discriminate diverse minerals with subtle spectral differences as well, with testing accuracies of 92% in the Cuddalore region and 94.4% in the Mayiladuthurai region. The validation process was conducted by using the same kind of images. Overall, this study highlights the potential of the use of PRISMA hyperspectral data combined with ML for accurate and detailed mineral identification and mapping in coastal regions.
This is a preview of subscription content, log in via an institution to check access.
Data availability
No datasets were generated or analysed during the current study.
References
Abdelsalam MG, Stern RJ, Berhane WG (2000) Mapping gossans in arid regions with Landsat TM and SIR-C images: the Beddaho Alteration Zone in northern Eritrea. J Afr Earth Sc 30(4):903–916. https://doi.org/10.1016/s0899-5362(00)00059-2
Agrawal N, Govil H, Mishra G, Gupta M, Srivastava PK (2023) Evaluating the performance of PRISMA shortwave Infrared Imaging sensor for mapping hydrothermally altered and weathered minerals using the machine learning paradigm. Remote Sensing 15(12):3133. https://doi.org/10.3390/rs15123133
Agrawal N, Govil H, Chatterjee S, Mishra G, Mukherjee S (2024) Evaluation of machine learning techniques with AVIRIS-NG dataset in the identification and mapping of minerals. Adv Space Res 73(2):1517–1534. https://doi.org/10.1016/j.asr.2022.09.018
Bedini E, Chen J (2020) Application of PRISMA satellite hyperspectral imagery to mineral alteration mapping at Cuprite, Nevada, USA. J Hyperspectr Remote Sens 10(2):87–94. https://doi.org/10.29150/jhrs.v10.2.p87-94
Bedini E, Chen J (2022) Prospection for economic mineralization using PRISMA satellite hyperspectral remote sensing imagery: an example from central East Greenland. J Hyperspectr Remote Sens 12(3):124–130. https://doi.org/10.29150/2237-2202.2022.253484
Benhalouche FZ, Benabbou O, Karoui MS, Kebir LW, Bennia A, Deville Y (2022) Minerals detection and mapping in the Southwestern Algeria Gara-Djebilet region with a multistage informed NMF-based unmixing approach using Prisma Remote Sensing Hyperspectral Data. IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium, 9861, 6422–6425. https://doi.org/10.1109/igarss46834.2022.9884746
Bioucas-Dias J, Nascimento J (2008) Hyperspectral subspace identification. IEEE Trans Geosci Remote Sens 46(8):2435–2445. https://doi.org/10.1109/tgrs.2008.918089
Castaldi F, Palombo A, Pascucci S, Pignatti S, Santini F, Casa R (2015) Reducing the influence of soil moisture on the estimation of clay from hyperspectral data: a case study using simulated PRISMA data. Remote Sens 7(11):15561–15582. https://doi.org/10.3390/rs71115561
Chen Y, Zhu L, Ghamisi P, Jia X, Li G, Tang L (2017) Hyperspectral images classification with Gabor filtering and convolutional neural network. IEEE Geosci Remote Sens Lett 14(12):2355–2359. https://doi.org/10.1109/lgrs.2017.2764915
Chirico R, Mondillo N, Laukamp C, Mormone A, Di Martire D, Novellino A, Balassone G (2022) Mapping hydrothermal and supergene alteration zones associated with carbonate-hosted Zn-Pb deposits by using PRISMA satellite imagery supported by field-based hyperspectral data, mineralogical and geochemical analysis. Ore Geol Rev 152:105244. https://doi.org/10.1016/j.oregeorev.2022.105244
Esmaeili M, Fathianpour N, Soltani-Mohammadi S (2024) PRISMA hyperspectral imagery for mapping alteration zones associated with Kuhpanj porphyry copper deposit, Southern Iran. Eur J Remote Sens 57(1):2299369. https://doi.org/10.1080/22797254.2023.2299369
Fan C, Xie H, Wu J, Birnbaum S (2012) Analysis of United States Geological Survey spectral library of silicate minerals: implication for remote sensing applications. J Appl Remote Sens 6(1):063514. https://doi.org/10.1117/1.jrs.6.063514
Fergus P, Chalmers C (2022) Supervised learning. In Computational intelligence methods and applications pp. 63–93. https://doi.org/10.1007/978-3-031-04420-5_3
Gemail K, Rahman NMA, Ghiath BM, Aziz RN (2016) Integration of ASTER and airborne geophysical data for mineral exploration and environmental mapping: a case study, Gabal Dara, North Eastern Desert, Egypt. Environ Earth Sci 75(7):592. https://doi.org/10.1007/s12665-016-5368-0
Ghamisi P, Plaza J, Chen Y, Li J, Plaza AJ (2017) Advanced spectral classifiers for hyperspectral images: a review. IEEE Geosci Remote Sens Mag 5(1):8–32. https://doi.org/10.1109/mgrs.2016.2616418
Govil H, Tripathi MK, Diwan P, Guha S, Monika (2018) Identification of iron oxides minerals in western Jahajpur region, India using AVIRIS-NG hyperspectral remote sensing. Int Arch Photogramm Remote Sens Spat Inf Sci XLII–5:233–237. https://doi.org/10.5194/isprs-archives-xlii-5-233-2018
Govil H, Tripathi MK, Diwan P, Monika (2020) Comparative evaluation of AVIRIS-NG and hyperion hyperspectral image for Talc mineral identification. In: Sharma N, Chakrabarti A, Balas V (eds) Data management, analytics and innovation. Advances in intelligent systems and computing, vol 1016. Springer, Singapore, pp 95–101. https://doi.org/10.1007/978-981-13-9364-8_7
Guha A (2020) Mineral exploration using hyperspectral data. In Elsevier eBooks pp. 293–318. https://doi.org/10.1016/b978-0-08-102894-0.00012-7
Gupta P, Venkatesan M (2020) Mineral Identification Using Unsupervised Classification from Hyperspectral Data. In Advances in intelligent systems and computing pp. 259–268. https://doi.org/10.1007/978-981-15-0135-7_25
Habashi J, Moghadam HJ, Oskouei MM, Pour AB, Hashim M (2024) PRISMA Hyperspectral Remote Sensing data for mapping alteration minerals in Sar-e-Châh-e-Shur region, Birjand, Iran. Remote Sens 16(7):1277. https://doi.org/10.3390/rs16071277
Hegab MA (2021) Remote sensing and gamma-ray spectrometry based gold related alteration zones detection: case study (Um Balad area), North Eastern Desert, Egypt. Pure Appl Geophys 178(10):3909–3931. https://doi.org/10.1007/s00024-021-02865-1
Hegab MAE, Mousa SE, Salem SM, Farag K, GabAllah H (2022) Gold-related alteration zones detection at the Um Balad Area, Egyptian Eastern Desert, using remote sensing, geophysical, and GIS data analysis. J Afr Earth Sci 196:104715. https://doi.org/10.1016/j.jafrearsci.2022.104715
Hegab MAE, Magd IAE, Wahid KHAE (2024) Revealing potential mineralization zones utilizing LANDSAT-9, ASTER, and airborne radiometric data at Elkharaza-Dara area, north eastern Desert, Egypt. Egypt J Remote Sens Space Sci 27(4):716–733. https://doi.org/10.1016/j.ejrs.2024.10.005
Hegab MAE (2024a) A multi-disciplinary approach for uranium exploration using remote sensing and airborne gamma-ray spectrometry data in the Gebel Duwi area, Central Eastern Desert, Egypt. Sci Rep 14(1):19739. https://doi.org/10.1038/s41598-024-69147-3
Hegab MAE (2024b) Mineral exploration and environmental impact assessment in the Jabal Hamadat Area, Central Eastern Desert, Egypt, using remote sensing and airborne radiometric data. Sci Rep 14(1):21986. https://doi.org/10.1038/s41598-024-71387-2
Kumar C, Chatterjee S, Oommen T, Guha A (2019) Automated lithological mapping by integrating spectral enhancement techniques and machine learning algorithms using AVIRIS-NG hyperspectral data in Gold-bearing granite-greenstone rocks in Hutti, India. Int J Appl Earth Obs Geoinf 86:102006. https://doi.org/10.1016/j.jag.2019.102006
Laukamp C (2022). Geological mapping using mineral absorption feature-guided band-ratios applied to prisma satellite hyperspectral level 2D imagery. IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium. https://doi.org/10.1109/igarss46834.2022.9884015
Leathers R, Downes T (2006) Scene-based nonuniformity correction and bad-pixel identification for hyperspectral VNIR/SWIR sensors. 2006 IEEE International Symposium on Geoscience and Remote Sensing, Denver, CO, USA, pp. 2373–2376. https://doi.org/10.1109/igarss.2006.614
Loizzo R, Daraio M, Guarini R, Longo F, Lorusso R, Dini L, Lopinto E (2019) Prisma mission status and perspective. IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium. https://doi.org/10.1109/igarss.2019.8899272
Mahmoud S, Hegab MaE, Soliman N (2023) Environmental impacts of mining activities in Um Balad—El Urf region; Central Eastern Desert. In Springer proceedings in earth and environmental sciences pp. 55–61. https://doi.org/10.1007/978-3-031-40447-4_7
Mancini F, Tullo A, Allemand P, Ori GG (2022) Mineral identification and abundance mapping through the hyperspectral PRISMA images on the Dallol Planetary Analog. Europlanet Science Congress 2022, Granada, Spain, EPSC2022-983. https://doi.org/10.5194/epsc2022-983
Mishra G, Govil H, Srivastava PK (2021) Identification of malachite and alteration minerals using airborne AVIRIS-NG hyperspectral data. Quat Sci Adv 4:100036. https://doi.org/10.1016/j.qsa.2021.100036
Mishra G, Govil H, Guha A, Kumar H, Kumar S, Mukherjee S (2024) Comparative evaluation of airborne AVIRIS-NG and spaceborne PRISMA hyperspectral data in identification and mapping of altered/weathered minerals in Jahazpur, Rajasthan. Adv Space Res 73(2):1459–1474. https://doi.org/10.1016/j.asr.2022.09.047
Mondal S, Guha A, Pal SK (2021) Comparative analysis of AVIRIS-NG and Landsat-8 OLI data for lithological mapping in parts of Sittampundi layered complex, Tamil Nadu, India. Adv Space Res 69(3):1408–1426. https://doi.org/10.1016/j.asr.2021.11.001
Paoletti M, Haut J, Plaza J, Plaza A (2019) Deep learning classifiers for hyperspectral imaging: A review. ISPRS J Photogramm Remote Sens 158:279–317. https://doi.org/10.1016/j.isprsjprs.2019.09.006
Peyghambari S, Zhang Y (2021) Hyperspectral remote sensing in lithological mapping, mineral exploration, and environmental geology: an updated review. J Appl Remote Sens 15(03):031501. https://doi.org/10.1117/1.jrs.15.031501
Pignatti S, Palombo A, Pascucci S, Romano F, Santini F, Simoniello, T, Umberto A, Vincenzo C, Acito N, Diani M, Matteoli S, Corsini G, Casa R, De Bonis R, Laneve G, Ananasso C (2013) The PRISMA hyperspectral mission: Science activities and opportunities for agriculture and land monitoring. 2013 IEEE International Geoscience and Remote Sensing Symposium - IGARSS, Melbourne, VIC, Australia, pp 4558-4561. https://doi.org/10.1109/igarss.2013.6723850
Pokhariyal S, Patel NR, Govind A (2023) Machine learning-driven remote sensing applications for agriculture in India—a systematic review. Agronomy 13(9):2302. https://doi.org/10.3390/agronomy13092302
Priya S, Ghosh R (2022) Soil clay minerals abundance mapping using AVIRIS-NG data. Adv Space Res 73(2):1360–1367. https://doi.org/10.1016/j.asr.2022.09.049
Qian S (2021) Hyperspectral satellites, evolution, and development history. IEEE J Sel Top Appl Earth Obs Remote Sens 14:7032–7056. https://doi.org/10.1109/jstars.2021.3090256
Shaik RU, Periasamy S, Zeng W (2023) Potential assessment of PRISMA hyperspectral imagery for remote sensing applications. Remote Sens 15(5):1378. https://doi.org/10.3390/rs15051378
Shebl A, Abriha D, Fahil AS, El-Dokouny HA, Elrasheed AA, Csámer Á (2023) PRISMA hyperspectral data for lithological mapping in the Egyptian Eastern Desert: Evaluating the support vector machine, random forest, and XG boost machine learning algorithms. Ore Geol Rev 161:105652. https://doi.org/10.1016/j.oregeorev.2023.105652
Sudharsan S, Hemalatha R, Radha S (2019) A survey on hyperspectral imaging for mineral exploration using machine learning algorithms. International Conference on Wireless Communications Signal Processing and Networking (WiSPNET), Chennai, India, pp. 206–212. https://doi.org/10.1109/wispnet45539.2019.9032740
Sun G, Zhang X, Jia X, Ren J, Zhang A, Yao Y, Zhao H (2020) Deep fusion of localized spectral features and multi-scale spatial features for effective classification of hyperspectral images. Int J Appl Earth Obs Geoinf 91:102157. https://doi.org/10.1016/j.jag.2020.102157
Tripathi P, Garg RD (2023) Potential of DESIS and PRISMA hyperspectral remote sensing data in rock classification and mineral identification: a case study for Banswara in Rajasthan, India. Environ Monit Assess 195(5):575. https://doi.org/10.1007/s10661-023-11200-1
Tripathi MK, Govil H (2019) Evaluation of AVIRIS-NG hyperspectral images for mineral identification and mapping. Heliyon 5(11):e02931. https://doi.org/10.1016/j.heliyon.2019.e02931
Youssef MAS, Elkhodary ST (2013) Utilization of airborne gamma ray spectrometric data for geological mapping, radioactive mineral exploration and environmental monitoring of southeastern Aswan city, South Eastern Desert, Egypt. Geophys J Int 195(3):1689–1700. https://doi.org/10.1093/gji/ggt375
Zhang X, Sun Y, Zhang J, Wu P, Jiao L (2018) Hyperspectral unmixing via deep convolutional neural networks. IEEE Geosci Remote Sens Lett 15(11):1755–1759. https://doi.org/10.1109/lgrs.2018.2857804
Funding
The authors did not receive support from any organization for the submitted work. No funding was received to assist with the preparation of this manuscript.
Author information
Authors and Affiliations
Contributions
S.S, R.H, and T.NV wrote the main manuscript text and S.S, T.NV, and KA.S prepared the figures. All authors reviewed the manuscript.
Corresponding author
Ethics declarations
Competing interests
The authors declare no competing interests.
Additional information
Communicated by: Hassan Babaie
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Sudharsan, S., Hemalatha, R., V., T.N. et al. Machine learning-driven mineral identification using PRISMA hyperspectral data along the coastal regions of Southeast Tamil Nadu. Earth Sci Inform 18, 327 (2025). https://doi.org/10.1007/s12145-025-01839-4
Received:
Accepted:
Published:
DOI: https://doi.org/10.1007/s12145-025-01839-4