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This paper reports the results of a study to differentiate iron ores in terms of their grades, using the hyperspectral (EO-1 Hyperion) image data, covering a mineralized belt in the Noamundi area, eastern India. The study involves hyperspectral data collection, pre-processing (reduction of atmospheric and solar flux effects), generation of spectral curves from the image for the iron ore deposits, extraction of key spectral parameters and linear spectral unmixing for mapping iron ore abundance. Spectral curves for iron ore deposits extracted from the Hyperion image pixels exhibit strong absorption at 850–900 nm and 2150–2250 nm wavelengths, which is typical of iron ores. The strength of the absorption features in the continuum removed spectra varies spatially in the image around the mining areas, indicating differences in composition/grade of the iron ores. Spectral parameters such as the depth, width, area and wavelength position of the absorption features, derived from image spectra in the 850–900 nm and 2150–2250 nm regions, correlate well with the concentration of iron-oxide and alumina (gangue) in the ore samples obtained from the mine face. Well defined correlations are evident between the concentration of iron oxide and (i) the depth of NIR absorption feature ( R 2 = 0.883); (ii) the width of NIR absorption feature ( R 2 = 0.912); and (iii) the area of the NIR absorption feature and ( R 2 = 0.882). Further, the linear spectral unmixing resulted in an iron ore abundance map which, in conjunction with the image- and laboratory-spectra, helped in assessing the grades of iron ores in the study area. Thus, this study demonstrates the feasibility of discriminating grades of iron ores based on spectral information derived from spaceborne hyperspectral imagery. [ABSTRACT FROM AUTHOR]

This paper present the results of a study made to assess the grades of iron ore deposits in parts of Noamundi area using the hyperspectral image data (EO-1 Hyperion). The study involves hyperspectral image data, preprocessing (removal of atmospheric effects), spectral curve generation from the image, extraction of certain spectral parameters and spectral data analysis. Atmospheric correction was carried out by using algorithms involving atmospheric analysis of spectral hyper-cubes and Quick Atmospheric Correction modules. Spectral curves generated for the pixels that contain iron ores resemble the spectral signature of iron ores, i.e., very strong absorption at 850-900nm region is observed. The strength of the absorption varies spatially at different pixels. Hence, spectral parameters such as radius of curvature of NIR absorption trough (750-1000nm), position of NIR absorption trough and distance from a reference(100% reflectance) line have been derived for all the image-derived spectra and were compared with the concentration of iron in the ore samples of mines. Empirical models were generated by relating the image-derived spectral parameters of iron ore locations and geochemistry of the samples collected from the same location which shows a reasonable match. Strong negative correlation is seen between the radius of curvature of the NIR absorption trough and concentration of iron with a R value of 0.809. The position of the NIR absorption trough is seen to shift towards longer wavelengths with decreasing iron content and the distance from the reference line increases with increasing iron content. Thus, this study indicates that it is feasible to discriminate the grades of iron ores from the spectral parameters derived from Hyperspectral satellite images. [ABSTRACT FROM AUTHOR]