Cluster Computing

, Volume 22, Supplement 3, pp 6921–6926 | Cite as

Mineral exploration by decision tree classification using multi temporal cluster images in Jharkhand region

  • S. RajalakshmiEmail author
  • V. Vijaya Chamundeeswari


India, being an abundant source of minerals, Mineral exploration, on a large scale, is promising to provide good impact for the future of the country. India has a rich source of coal, bauxite, limestone etc. With the advent of remote sensing technologies capturing broader area, exploration of minerals has now become an appreciable problem. Satellite Cluster Images spanning over wider areas can effectively serve as a tool for mapping minerals, qualitatively and quantitatively. In this paper, an algorithm, based on decision tree classification, is developed to map minerals, specifically, coal and limestone for a specific region. Multi-temporal cluster images are employed to map dynamic change detection resulting in greater accuracy. In this paper, multi-temporal cluster images (Landsat 8 OLI/TIRS) are analyzed to map coal, limestone and ‘no-mineral’ regions with the help of the algorithm developed using decision tree classification. Classification results are compared with ground truth data for assessing its accuracy.


Multi-temporal cluster images Decision tree classification Landsat 8 Mineral exploration 


  1. 1.
    Moghtaderi, A., Moore, F., Ranjbar, H.: Application of ASTER and Landsat 8 imagery data and mathematical evaluation method in detecting iron minerals contamination in the Chadormalu iron mine area, central Iran. J. Appl. Remote Sens. 11(1), 016027 (2017)CrossRefGoogle Scholar
  2. 2.
    Liu, L., Zhou, J., Han, L., Xu, X.: Mineral mapping and ore prospecting using Landsat TM and Hyperion data, Wushitala, Xinjiang, northwestern China. Ore Geol. Rev. 81, 280–295 (2017)CrossRefGoogle Scholar
  3. 3.
    Wang, W., Cheng, Q.: Mapping mineral potential by combining multi-scale and multi- source geo-information. In: Geoscience and Remote Sensing Symposium, pp. 1321–1324 (2008)Google Scholar
  4. 4.
    Hujun, H.E., Zhao, Y.: Multi-source data fusion technology and its application in geological and mineral survey. In: IEEE Transactions on Geoscience and Remote Sensing, pp. 271–274 (2010)Google Scholar
  5. 5.
    Kruse, J., Boardman, W., Huntington, J.F.: Fifteen years of hyperspectral data: Northern Grapevine Mountains, Nevada. In: Proceedings of the 8th JPL Airborne Earth Science Workshop, vol. 99–17. Pasadena, CA, pp. 247–258 (1999)Google Scholar
  6. 6.
    Cawood, P.A., Hawkesworth, C.J.: Temporal relations between mineral deposits and global tectonic cycles. Geol. Soc. Lond. 393(1), 9–21 (2013)CrossRefGoogle Scholar
  7. 7.
  8. 8.
  9. 9.
  10. 10.
  11. 11.
  12. 12.
    Boardman J.W., Kruse F.A,: Automated spectral analysis: a geologic example using AVIRIS data, North Grapevine Mountains, Nevada. In: Proceedings of 10th Thematic Conference on Geologic Remote Sensing, Ann Arbor, MI, pp. I-407–I-418, (1994)Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of CSEVelammal Engineering CollegeChennaiIndia

Personalised recommendations