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Enhancement of limestone mineral identification using Hyperion imagery: a case study from Tirunelveli District, Tamil Nadu, South India

  • Vignesh Kumar
  • Kiran YarrakulaEmail author
Original Paper
  • 46 Downloads

Abstract

Hyperspectral remote sensing consolidates imaging and spectroscopy in a solitary system which frequently comprises big datasets and necessitates the novel processing methods. In the present study, Cheranmadevi Block of Tirunelveli District in Tamil Nadu is selected to extract the abundant limestone mineral. Hyperion is one of the freely available hyperspectral imagery containing 242 spectral bands with 10-nm intervals in the wavelength between 400 and 2500 nm. The main objectives of the present research work are to enhance the imagery visualization, end member extraction, and classification, and estimate the abundant limestone quantity by removing the striping error in Hyperion imagery. The scanning electron microscope with energy-dispersive X-ray spectroscopy analysis is performed to identify the chemical composition of limestone mineral. The spectral reflectance of limestone is characterized using analytical spectral devices like a field spectroradiometer. Limestone has deep absorption in the short-wave infrared region (1900–2500 nm) around 2320–2340 nm due to their calcite composition (CaCO3). The feature extraction in Hyperion data is performed using various preprocessing steps like bad bands removal, vertical strip removal, and radiance and reflectance creation. To improve the classification accuracy, vertical strip removal process is performed using a local destriping algorithm. The absolute reflectance is achieved by the atmospheric correction module using Fast Line-of-sight Atmospheric Analysis of Hypercubes. The acquired reflectance image spectra are compared with the spectral libraries of USGS, JPL, and field spectra. Destriping enhances qualities of Hyperion data interims of the spectral profile, radiance, reflectance, and data reduction methods. The present research work focused on the local destriping algorithm to increase the quality and quantity of limestone deposit extraction.

Keywords

Hyperion Destripe Limestone FLAASH SEM 

References

  1. Adam E, Mutanga O, Rugege D (2010) Multispectral and hyperspectral remote sensing for identification and mapping of wetland vegetation: a review. Wetl Ecol Manag 18(3):281–296CrossRefGoogle Scholar
  2. Balachandran A (2009) Technical report series: district groundwater brochure. Government of India, Ministry of Water Resources, Central Ground Water Board, South Eastern Coastal Region, Chennai, pp 1–20Google Scholar
  3. Ballanti L, Blesius L, Hines E, Kruse B (2016) Tree species classification using hyperspectral imagery: a comparison of two classifiers. Remote Sens 8(6):445CrossRefGoogle Scholar
  4. Beiranv A, Hashim M (2011) The Earth Observing-1 (EO-1) satellite data for geological mapping, southeastern segment of the Central Iranian Volcanic Belt, Iran. International Journal of Physical Sciences 6(33):7638–7650Google Scholar
  5. Chuan Z, Fawang Y, Haixia H, Hongcheng L (2014) Study on the forest vegetation restoration monitoring using HJ-1A hyperspectral data. In IOP Conference Series: Earth and Environmental Science 17(1):012082Google Scholar
  6. Ciampalini A, Consoloni I, Salvatici T, Di Traglia F, Fidolini F, Sarti G, Moretti S (2015) Characterization of coastal environment by means of hyper- and multispectral techniques. Appl Geogr 57:120–132CrossRefGoogle Scholar
  7. Dave PN, Bhandari J (2013) Prosopis juliflora: a review. International Journal of Chemical Studies 1(3):181Google Scholar
  8. El-Magd IA, El Kafrawy S, Farag I (2014) Detecting oil spill contamination using airborne hyperspectral data in the River Nile, Egypt. Open Journal of Marine Science 4(2):140–150CrossRefGoogle Scholar
  9. Gong P, Pu R, Biging GS, Larrieu MR (2003) Estimation of forest leaf area index using vegetation indices derived from Hyperion hyperspectral data. IEEE Transactions Geoscience Remote Sensing Letters 41(6):1355–1362CrossRefGoogle Scholar
  10. Hoshino B, Aramalla AK, Lbasit MAMABDE et al (2012) Evaluating the invasion strategic of mesquite (Prosopis juliflora) in eastern Sudan using remotely sensed technique. Journal of Arid Land Studies 4:1–4Google Scholar
  11. Kruse FA (2003) Mineral mapping with AVIRIS and EO-1 Hyperion in: Presented at the 12th JPL Airborne Geoscience Workshop, 24–28 February, 2003. Pasadena, CaliforniaGoogle Scholar
  12. Kruse FA, Boardman JW (2000) Characterization and mapping of kimberlites and related diatremes using hyperspectral remote sensing. In Aerospace Conference Proceedings, 2000 IEEE vol 3, pp 299–304. IEEEGoogle Scholar
  13. Kruse FA, Perry SL (2007) Regional mineral mapping by extending hyperspectral signatures using multispectral data. In Aerospace Conference, 2007 IEEE, pp 1–14. IEEEGoogle Scholar
  14. Kruse FA, Boardman JW, Huntington JF (2003) Comparison of airborne hyperspectral data and EO-1 Hyperion for mineral mapping. IEEE Transactions Geoscience Remote Sensing Letters 41(6):1388–1400CrossRefGoogle Scholar
  15. Kruse FA, Perry SL, Caballero A (2006) District-level mineral survey using airborne hyperspectral data, Los Menucos, Argentina. Ann Geophys 49(1):83–92Google Scholar
  16. Kruse FA, Bedell RL, Taranik JV, Peppin WA, Weatherbee O, Calvin WM (2012) Mapping alteration minerals at prospect, outcrop and drill core scales using imaging spectrometry. Int J Remote Sens 33(6):1780–1798CrossRefGoogle Scholar
  17. Kumar MV, Yarrakula K (2017) Comparison of efficient techniques of hyper-spectral image preprocessing for mineralogy and vegetation studies. Indian Journal of Geomarine Science 46:1008–1021Google Scholar
  18. Minu S, Shetty A (2015) Atmospheric correction algorithms for hyperspectral imageries: a review. International Research Journal of Earth Sciences 3(5):14–18Google Scholar
  19. Mureriwa N, Adam E, Sahu A, Tesfamichael S (2016) Examining the spectral separability of Prosopis glandulosa from co-existent species using field spectral measurement and guided regularized random Forest. Remote Sens 8(2):144CrossRefGoogle Scholar
  20. Pal MK, Porwal A (2015) Destriping of Hyperion images using low-pass-filter and local-brightness-normalization. In Geoscience and Remote Sensing Symposium (IGARSS), 2015 IEEE International pp 3509–3512Google Scholar
  21. Pour AB, Hashim M (2014) ASTER, ALI and Hyperion sensors data for lithological mapping and ore minerals exploration. Springerplus 3(1):130CrossRefGoogle Scholar
  22. Pour AB, Hashim M (2015) Evaluation of earth observing-1 (EO1) data for lithological and hydrothermal alteration mapping: a case study from Urumieh-Dokhtar volcanic belt, SE Iran. Journal of the Indian Society of Remote Sensing 43(3):583–597CrossRefGoogle Scholar
  23. Pour AB, Hashim M, van Genderen J (2013) Detection of hydrothermal alteration zones in a tropical region using satellite remote sensing data: Bau goldfield, Sarawak, Malaysia. Ore Geol Rev 54:181–196CrossRefGoogle Scholar
  24. Qingwei Z, Weisheng M, Chang L, Moquan S, Aihua W, Donglu J, District H, Beijing PRC (2013) Research on improved destriping algorithm with spectral moment matching for hyper-spectral images. Asian Association of Remote Sensing :1–5Google Scholar
  25. Smara (2015) Oblique striping removal in EO-1 hyperspectral remote sensing imagery. International Symposium on Advances on Remote Sensing Technologies and Computation RESENS 2015, At Barcelona SpainGoogle Scholar
  26. Smith R (2013) Analyzing hyperspectral images. TIN Maps, pp 1–40Google Scholar
  27. Vignesh M, Yarakkula K (2016) Identification of the alluvial soil deposit using hyperspectral imagery. Geospatial Technologies for Rural Development, pp 148–152Google Scholar
  28. Vigneshkumar M, Yarakkula K (2017) Nontronite mineral identification in nilgiri hills of Tamil Nadu using hyperspectral remote sensing. IOP Conference Series: Materials Science and Engineering 263(3):032001Google Scholar
  29. Vigneshkumar M, Yarrakula K (2017) Spatial distribution of Prosopis juliflora using the fusion of hyperspectral and Landsat-8 OLI imagery. Indian Journal of Ecology 44:548–554Google Scholar
  30. Wu C, Niu Z, Tang Q, Huang W (2008) Estimating chlorophyll content from hyperspectral vegetation indices: modeling and validation. Agric For Meteorol 148(8):1230–1241CrossRefGoogle Scholar
  31. Zaini N, van der Meer F, van der Werff H (2014) Determination of carbonate rock chemistry using laboratory-based hyperspectral imagery. Remote Sens 6(5):4149–4172CrossRefGoogle Scholar
  32. Zigovecki Gobac Z, Posilovic H, Bermanec V (2009) Identification of biogenetic calcite and aragonite using SEM. Geologia Croatica 62(3):201–206CrossRefGoogle Scholar

Copyright information

© Saudi Society for Geosciences 2019

Authors and Affiliations

  1. 1.Vellore Institute of TechnologyVelloreIndia

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