Satellite imagery and spectral matching for improved estimation of calcium carbonate and iron oxide abundance in mine ar
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ORIGINAL PAPER
Satellite imagery and spectral matching for improved estimation of calcium carbonate and iron oxide abundance in mine areas Padma SrinivasaPerumal 1 & Sanjeevi Shanmugam 2 & Pradeep Ganapathi 2 Received: 22 May 2020 / Accepted: 13 August 2020 # Saudi Society for Geosciences 2020
Abstract This study attempts grade-wise mapping in a limestone mine in Ariyalur, Southern India, and in the iron ore mines of Noamundi, Eastern India. After noise removal in the Sentinel 2A (multispectral) and EO-1 Hyperion (hyperspectral) image datasets, spectral matching is performed using the Jeffries-Matusita (JM) distance, Spectral Correlation Mapper (SCM), and combined JM-SCM measure. Due to the specific absorption spectra for carbonates (1900 nm, 2000 nm, and 2160 nm) and iron oxide (865 nm), it is possible to identify and map such mineral deposits using the multispectral dataset (Sentinel 2A) and hyperspectral dataset (EO-1 Hyperion) respectively. The grade-wise mapping of carbonate in the Ariyalur mine using the Sentinel 2A dataset by the JeffriesMatusita (JM) approach and Spectral Correlation Mapper (SCM) yielded R2 values of 0.44 and 0.77 respectively, whereas the combined JM-SCM approach resulted in a higher correlation with an R2 value of 0.87. The grade-wise mapping of iron oxide in Noamundi using the EO-1 Hyperion dataset by the Jeffries-Matusita (JM) approach and the Spectral Correlation Mapper (SCM) approach yielded R2 values of 0.15 and 0.76, respectively, whereas the combined JM-SCM approach resulted in a higher correlation with an R2 value of 0.90. Such an improved performance of the combined approach is primarily due to the simultaneous and effective utilization of band-wise information (by JM) and correlation aspects (by SCM) of the reference and target spectra considered in the matching algorithm. Thus, in this study, the proposed algorithm proved its compatibility and utility in extracting information on mineral abundance distribution for mine areas. Keywords Mineral mapping . Spectral matching . Jeffries-Matusita . Spectral Correlation Mapper
Introduction Grade estimation and mapping the grade-wise distribution of ores is essential in the mining industry, primarily to establish the suitability of the ores or minerals to serve as a raw material for production of metals and other end-products. It is also done to identify pockets of different grades of ores or minerals in a mine to be used for blending. Grade-wise classification is usually done by investigating the chemical composition of raw ores and minerals for which the samples are obtained directly from the deposits. Responsible Editor: Biswajeet Pradhan * Padma SrinivasaPerumal [email protected] 1
Department of Civil Engineering, Saveetha Engineering College, Anna University, Chennai 602105, India
2
Department of Geology, Anna University, Chennai 600025, India
Muwanguzi et al. (2012) attempted the characterization of chemical composition and microstructure of natural (raw) iron ore from Muko deposits in south-western Uganda by obtaining sa
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