Journal of Mining Engineering

Journal of Mining Engineering

Spectral Unmixing of Hyperion Data to Identify the Indicator Minerals of Khoy Region Using A Bilinear Method

Document Type : research - paper

Authors
Faculty of Mining Engineering, Sahand University of Technology, Tabriz, Iran
Abstract
The hyperspectral images are studied to extract the spectral signatures of the elements that comprise the image pixels (end members) and estimate their frequency. The surface reflectance spectrum is considered a linear combination of endmember spectra in linear mixing models. When internal mixing is also important, the linear model is not the answer, and non-linear algorithms should be used. The method used in this research is the generalization and improvement of Nascimento and Fan's bilinear models, known as the generalized bilinear mixing model (BPOGM). This study aims to apply and evaluate this method in the face of data with high mixing and large volumes. Therefore, the data used in this research are Hyperion data of Khoi region, which has good mineral and mineralogical indicators. First, the available pure spectra were extracted using the N-FINDR method. In addition to the excellent compatibility of the N-FINDER method, it has more ability to extract endmembers than the pixel purity index method used in linear separation. In this way, stilbite mineral (representative of zeolite group), vermiculite (representative of mica group), serpentine (representative of olivines of harzburgite and serpentinized ultramafic rocks), chlorite (representative of chlorite group), and quartz were identified. Then, using the BPOGM method, which is a solution method for the bilinear GBM model, the frequency of each end member was calculated, and the distribution map was obtained. The results of the non-linear method comply well with the geological map of the region based on mineralogical interpretations of the lithological facies (average accuracy of 78.25), which is completely acceptable at this stage of exploration work.
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Subjects

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Volume 19, Issue 62
Winter 2024
Pages 1-15

  • Receive Date 16 July 2023
  • Revise Date 03 March 2024
  • Accept Date 29 February 2024