Mineral Mapping on Hyperspectral Imageries Using Cohesion-based Self Merging Algorithm
Recently, hybrid clustering algorithms gained much research attention due to better clustering results and are computationally efficient. Hyperspectral image classification studies should be no exception, including mineral mapping. This study aims to tackle the biggest challenge of mapping the mineralogy of drill core samples, which consumes a lot of time. In this paper, we present the investigation using a hybrid clustering algorithm, cohesion-based self-merging (CSM), for mineral mapping to determine the number and location of minerals that formed the rock. The CSM clustering performance was then compared to its classical counterpart, K-means plus-plus (K-means++). We conducted experiments using hyperspectral images from multiple rock samples to understand how well the clustering algorithm segmented minerals that exist in the rock. The samples in this study contain minerals with identical absorption features in certain locations that increase the complexity. The elbow method and silhouette analysis did not perform well in deciding the optimum cluster size due to slight variance and high dimensionality of the datasets. Thus, iterations to the various numbers of k-clusters and m-subclusters of each rock were performed to get the mineral cluster. Both algorithms were able to distinguish slight variations of absorption features of any mineral. The spectral variation within a single mineral found by our algorithm might be studied further to understand any possible unidentified group of clusters. The spatial consideration of the CSM algorithm induced several misclassified pixels. Hence, the mineral maps produced in this study are not expected to be precisely similar to ground truths.
D. Krupnik and S. D. Khan, “High-resolution hyperspectral mineral mapping: Case studies in the Edwards Limestone, Texas, USA and sulfide-rich quartz veins from the Ladakh Batholith, Northern Pakistan,” Minerals, vol. 10, no. 11, pp. 1-16, 2020, doi: 10.3390/min10110967. Crossref
Á. F. Egaña, F. A. Santibáñez-Leal, C. Vidal, G. Díaz, S. Liberman, and A. Ehrenfeld, “A robust stochastic approach to mineral hyperspectral analysis for geometallurgy,” Minerals, vol. 10, no. 12, pp. 1–32, 2020, doi: 10.3390/min10121139. Crossref
R. Gore, A. Mishra, and R. Deshmukh, “Exploring the Mineralogy at Lonar Crater with Hyperspectral Remote Sensing,” Journal of the Geological Society of India, vol. 97, no. 3, pp. 261-266, Mar. 2021, doi: 10.1007/s12594-021-1676-4. Crossref
M. Pineau et al., “Estimating kaolinite crystallinity using near-infrared spectroscopy: Implications for its geology on Earth and Mars,” American Mineralogist, vol. 107, no. 8, pp. 1453–1469, Aug. 2022, doi: 10.2138/am-2022-8025. Crossref
R. G. Skirrow et al., “Mapping iron oxide Cu-Au (IOCG) mineral potential in Australia using a knowledge-driven mineral systems-based approach,” Ore Geology Reviews, vol. 113, p. 103011, Oct. 2019, doi: 10.1016/j.oregeorev.2019.103011. Crossref
F. J. A. Van Ruitenbeek et al., “Mapping the wavelength position of deepest absorption features to explore mineral diversity in hyperspectral images,” Planet. Space Sci., vol. 101, pp. 108-117, Oct. 2014, doi: 10.1016/J.PSS.2014.06.009. Crossref
J. M. Meyer, R. F. Kokaly, and E. Holley, “Hyperspectral remote sensing of white mica: A review of imaging and point-based spectrometer studies for mineral resources, with spectrometer design considerations,” Remote Sens. Environ., vol. 275, p. 113000, Jun. 2022, doi: 10.1016/J.RSE.2022.113000. Crossref
B. B. Sinaice et al., “Spectral Angle Mapping and AI Methods Applied in Automatic Identification of Placer Deposit Magnetite Using Multispectral Camera Mounted on UAV,” Minerals, vol. 12, no. 2, p. 268, Feb. 2022, doi: 10.3390/min12020268. Crossref
M. Wang, Z. Huang, X. Zhang, Y. Zhang, and M. Chen, “Altered mineral mapping based on ground-airborne hyperspectral data and wavelet spectral angle mapper tri-training model: Case studies from Dehua-Youxi-Yongtai Ore District, Central Fujian, China,” International Journal of Applied Earth Observation and Geoinformation, vol. 102, p. 102409, Oct. 2021, doi: 10.1016/j.jag.2021.102409. Crossref
M. E. Paoletti, J. M. Haut, J. Plaza, and A. Plaza, “Yinyang K-means clustering for hyperspectral image analysis,” in Proc. 17th Int. Conf. Comput. Math. Methods Sci. Eng, 2017, pp. 1625-1636, 2017.
C. Ding et al., “Hyperspectral Image Classification Promotion Using Clustering Inspired Active Learning,” Remote Sens., vol. 14, no. 3, 2022, doi: 10.3390/rs14030596. Crossref
Y. Chen, S. Ma, X. Chen, and P. Ghamisi, “Hyperspectral data clustering based on density analysis ensemble,” Remote Sens. Lett., vol. 8, no. 2, pp. 194–203, Feb. 2017, doi: 10.1080/2150704X.2016.1249295. Crossref
C.-R. Lin and M.-S. Chen, “Combining partitional and hierarchical algorithms for robust and efficient data clustering with cohesion self-merging,” IEEE Transactions on Knowledge and Data Engineering, vol. 17, no. 2, pp. 145-159, Feb. 2005, doi: 10.1109/tkde.2005.21. Crossref
S. Koley and A. Majumder, “Brain MRI segmentation for tumor detection using cohesion based self merging algorithm,” in Proc. 2011 IEEE 3rd International Conference on Communication Software and Networks, May 2011, doi: 10.1109/iccsn.2011.6015005. Crossref
M. R. Noor, “Ore Texture Measurment Using Infrared Hyperspectral Imagery of Porphyry Cu Pebbles for Copper Content Estimation,” 2019.
I. C. Contreras, C. Hecker, and F. van der Meer, “Mapping Epithermal Alteration Mineralogy with High Spatial Resolution Hyperspectral Imaging of Rock Samples,” GRSG Conf., May 2018, pp. 123-128, 2015.
B. Portela, M. D. Sepp, F. J. A. van Ruitenbeek, C. Hecker, and J. H. Dilles, “Using hyperspectral imagery for identification of pyrophyllite-muscovite intergrowths and alunite in the shallow epithermal environment of the Yerington porphyry copper district,” Ore Geology Reviews, vol. 131, p. 104012, Apr. 2021, doi: 10.1016/j.oregeorev.2021.104012. Crossref
C. Shi, B. Wei, S. Wei, W. Wang, H. Liu, and J. Liu, “A quantitative discriminant method of elbow point for the optimal number of clusters in clustering algorithm,” EURASIP Journal on Wireless Communications and Networking, vol. 2021, no. 1, Feb. 2021, doi: 10.1186/s13638-021-01910-w. Crossref
M. Shutaywi and N. N. Kachouie, “Silhouette Analysis for Performance Evaluation in Machine Learning with Applications to Clustering,” Entropy, vol. 23, no. 6, p. 759, Jun. 2021, doi: 10.3390/e23060759. Crossref
D. Arthur and S. Vassilvitskii, “K-means++: The advantages of careful seeding,” in Proc. The Eighteenth Annual ACM-SIAM Symposium on Discrete Algorithms, 2006, pp. 1027–1035.
J. H. Ward, “Hierarchical Grouping to Optimize an Objective Function,” Journal of the American Statistical Association, vol. 58, no. 301, pp. 236-244, Mar. 1963, doi: 10.1080/01621459.1963.10500845. Crossref
C. Ye and C. Zhong, “An improved cohesion self-merging clustering algorithm,” in Proc. 2011 Eighth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD), Jul. 2011, doi: 10.1109/fskd.2011.6019670. Crossref
C. Zhong, T. Luo, and X. Yue, “Cluster Ensemble Based on Iteratively Refined Co-Association Matrix,” IEEE Access, vol. 6, pp. 69210-69223, 2018, doi: 10.1109/access.2018.2879851. Crossref
T. Alqurashi and W. Wang, “Clustering ensemble method,” International Journal of Machine Learning and Cybernetics, vol. 10, no. 6, pp. 1227-1246, Jan. 2018, doi: 10.1007/s13042-017-0756-7. Crossref
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