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Mapping ilmenite deposit in Pulmudai, Sri Lanka using a hyperspectral imaging-based surface mineral mapping method

Authors:

EMMB Ekanayake ,

University of Peradeniya, LK
About EMMB
Department of Electrical and Electronic Engineering, Faculty of Engineering
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SSP Vithana,

Sri Lanka Technological Campus, LK
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EMHEB Ekanayake,

University of Peradeniya, LK
About EMHEB
Department of Electrical and Electronic Engineering, Faculty of Engineering
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ARMAN Rathnayake,

University of Peradeniya, LK
About ARMAN
Department of Electrical and Electronic Engineering, Faculty of Engineering
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AMR Abeysekara,

University of Peradeniya, LK
About AMR
Department of Electrical and Electronic Engineering, Faculty of Engineering
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TSJ Oorloff,

University of Peradeniya, LK
About TSJ
Department of Electrical and Electronic Engineering, Faculty of Engineering
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HMVR Herath,

University of Peradeniya, LK
About HMVR
Department of Electrical and Electronic Engineering, Faculty of Engineering
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GMRI Godaliyadda,

University of Peradeniya, LK
About GMRI
Department of Electrical and Electronic Engineering, Faculty of Engineering
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MPB Ekanayake,

University of Peradeniya, LK
About MPB
Department of Electrical and Electronic Engineering, Faculty of Engineering
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A Senaratne

University of Peradeniya, LK
About A
Department of Geology, Faculty of Science
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Abstract

Mineral detection using remote sensing techniques is important since it saves the time and effort of carrying out manual land surveys. In this paper a novel algorithm, which can be used to detect ilmenite using hyperspectral image analysis is discussed. To investigate this task, a hyperspectral image obtained from the Earth Observing-1 (EO-1) satellite’s Hyperion sensor was used. In the proposed algorithm, first, principal component analysis (PCA) was used for dimensionality reduction and an Euclidean distance-based method was used to extract the pixels containing soil. Thereafter, lab spectral data of typical ilmenite deposits were considered as the reference and a correlation factor analysis was carried out to determine the soil pixels, which are most likely to contain ilmenite and those most unlikely to contain ilmenite. Using these two sets of pixels, a training set was constructed to apply Fisher’s discriminant analysis (FDA) in order to separate the dataset into two distinct classes – ilmenite and non-ilmenite. Based on the spectral similarity, each pixel of the image was classified under one of these classes. This paper also introduces a probability-based approach to obtain results that are more accurate. A probability density function was designed considering the spatial distribution of the mineral. Thereafter, classification was done considering the probability measure as well. Lab tests performed on the soil samples collected from the locations, which were detected by the algorithm validate that the algorithm is accurate.

How to Cite: Ekanayake, E., Vithana, S., Ekanayake, E., Rathnayake, A., Abeysekara, A., Oorloff, T., Herath, H., Godaliyadda, G., Ekanayake, M. and Senaratne, A., 2019. Mapping ilmenite deposit in Pulmudai, Sri Lanka using a hyperspectral imaging-based surface mineral mapping method. Journal of the National Science Foundation of Sri Lanka, 47(3), pp.271–284. DOI: http://doi.org/10.4038/jnsfsr.v47i3.9276
Published on 30 Sep 2019.
Peer Reviewed

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