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• Comparative studies on the basis of presence and absence of minerals. • Discrimination of the mineral body from the settlements. • Pyrrhotite and limonite (iron ore) minerals have been traced. • Mixture Tuned Matched Filtering algorithm produces a better mineral map. Geology majorly deals with the structures, features, minerals, rocks, etc. of the planet Earth. Geologists are using the technology of remote sensing for structural interpretation and regional mapping. Ores and minerals identification are also done with the help of remote sensing. This paper is mainly focused on the identification of minerals and mapping with the help of various algorithms such as Spectral Angle Mapper (SAM), Spectral Feature Fitting (SFF) and Mixture Tuned Matched Filtering (MTMF) using airborne hyperspectral data. Minerals are identified on the basis of the visible and near-infrared spectral reflectance. Spectral reflectance is having absorption features at different positions and absorption peaks are used for the analysis of imagery. This technique provides surface mineralogical details. SAM algorithm mainly computes the angle between the unknown pixel spectrum and unique pixel spectrum. SFF algorithm matched the continuum removed the spectrum of the imagery pixel from the continuum removed reference spectra. MTMF algorithm detects the abundance of the minerals and removes the erroneous positive. Total 13 endmembers (minerals) were identified in the study area. These minerals are grouped into clay minerals, iron minerals, carbonate minerals, and other minerals. These endmembers are used for the mineral map creation from different algorithms. Algorithms produce the diverse kind of mineral map and these are compared with each other on the basis of the mineralogy and discrimination of mineralized region from settlements. Mineral map produced by MTMF algorithm provides convenient results with better accuracy than other algorithms. [ABSTRACT FROM AUTHOR]

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]