Sensor Technology for Mineral Exploration

Sensor Technology for Mineral Exploration 1. Introduction Significant contribution is done by satellite remote sensing in the field of mineral exploration viz. Geological faults, fractures and mapping, which are associated with the ore deposits based on spectral signature, (Farooq and Govil 2013; Magendra and Sanjeevi 2014; Murphy and Monteiro 2011, Le Yo et al., 2011) the spectral signature helps in the recognizes hydrothermal altered rocks (Sabins, 1999). The multispectral remote sensing exhibits differences in spectral signatures which are insufficient spectral resolution for the hydrothermal altered mineral mapping (Clark, 1999). The Multispectral sensors viz. Landsat TM, ETM+, ASTER image processing helps in iron oxides mapping, the spectral ranges 1.55-1.75 Â µm and 2.08-2.35 Â µm is important for iron mapping (Gupta, 2003). The hyperspectral images provide a higher spectral resolution the the multispectral images (Clark et al., 1990; Magendra and Sanjeevi 2014; Van der Meer 2012). The discovery of new hyperspectral sensor technology in terms of both sensor and technical development has provided the opportunity to revisit previous remote sensing approaches for the mineral exploration as well as for the development of improved methods. Hyperspectral sensors have hundreds of channels, aircraft and satellite platforms which provide unique spectral datasets, and which are helpful in analyzing the surface mineralogy mapping (Goetz et al., 1985; Kruse et al., 2003; Debba et al., 2005, Vaughan et al., 2003). The airborne sensors like AVIRIS, HYDICE and Satellite sensor like Hyperion are used for mapping geology, snow etc. Hyperspectral remote sensing aims at providing the requirements like spectral, spatial and radiometric empower, measuring in terms of range, sampling, response, stability, uniformity, precision and accuracy. With the help of hyperspectral remote sensing we can find different minerals viz iron oxides, micas, chlorites, amphiboles, talc, serpentines, c arbonates, quartz, garnets, pyroxenes, feldspars and sulphates (Eva Papp and Cudahy 2002; Magendran and sanjeevi 2014; Hubbard and Crowley 2005). EO-1 Hyperion is the first Space based hyperspectral sensor, and it was launched on 21 November 2000 (Ungar et al., 2003). The Hyperion image has 30m spatial resolution, 242 channels and 7.7 km swath. The hyperspectral (Hyperion) sensor with 0.4-2.5Â µm spectral range, i.e. visible-near infrared (VNIR) spectrometer (approxmeterly0.4-1.0Â µm) and one short-wave infrared (SWIR) spectrometer (approximately 0.9-2.5Â µm) (EO-1 User guide) in which some minerals and rocks show good absorption and reflectance, due to variation in physicochemical properties, which help in their exploration mapping (Clark et al., 1990; Hunt et al., 1971). The spectral reflectance one can detect and identify the Earth surface and atmospheric constituents to measure the reflected spectra’s component concentration. We can find the distribution of the component and validate by improving models. The processing of Hyperion image is a challenging task as it consists hundreds of channels. The selection of required channels with its good apparent reflection requires good skills. The direct measurements of atmospheric properties are rarely available, and there are some techniques which surmise them from their imprint on hyperspectral radiance data. These properties are used to constrain highly accurate models of atmospheric radiation transfer to produce an estimate of the true surface reflectance. Moreover, atmospheric corrections of this type can be applied on a pixel by pixel basis since each pixel in a hyperspectral image contains an independent measurement of atmospheric water vapor absorption bands. There are different models available viz QUAC, 5S, 6S, ATCOR, ATREAM, HATCH, EFFORT Polishing, FLAASH etc (ITTVis, 2010). FLAASH is a MODTRAN4-based atmospheric correction software package, which provides accurate, physics-based derivation of apparent surface reflectance, through derivation of atmospheric properties such as surface albedo, surface altitude, water vapor column, aerosol and cloud optical depths, surface and atmospheric temperature from hyperspectral imaging data. FLAASH uses the most advanced techniques for handling particular stressing atmospheric conditions, such as the presence of clouds, cirrus and opaque cloud classification map adjustable spectral polishing for artifact suppression. The Hyperion image consists of a huge number of data sets which are supposed to be reduced dimensionally. The techniques like Minimum Noise Fraction (MNF) transform are used to reduce the number of spectral dimensions to be analyzed. The pure pixels are the most spectrally extreme pixels (Broadman et al., 1995), which spectrally correspond to the mixing end members. These end members form the base for the n-Dimensional visualization, and each selected end members are spectrally matched with USGS spectral library. The near visible near infrared image (VNIR) and shortwave infrared (SWIR) spectral range cover the features of iron bearing minerals, hydroxyl bearing minerals sulphates and carbonates. The iron ores and iron bearing minerals have characteristic spectra in the 850nm to 950 nm wavelength (Magendran and Sanjeevi, 2014). The ferric iron minerals hematite (Fe203) has distinct spectral curves in the visible near-infrared image (VNIR), which is caused by absorptions and induced by crystal field transitions at about 465 nm, 650 nm and 850–950 nm (Townsend, 1987). The paper presents an attempt for mapping iron oxides in Chitradurga Schist belt by using the Hyperion image. The iron distribution mapping is made with the standardized hyperspectral methodologies. An attempt is also made by taking the spectra of iron in-vitro and compared it with the USGS spectral libraryfor mappingiron distribution. The Spectral Angle Mapper Classification (SAM) is an automated method of comparing the image spectra with the individual spectra, or a spectral library (Boardman 1992; Kruse et al 1993). SAM treats both individual spectra, spectral library spectra and calculates as vectors and its spectral angle. Since the SAM algorithm uses the only vector direction and not the vector length. The result of the SAM classification is an image showing the best match at each pixel. This method is typically used for determining the mineralogy and works better in the areas of identical regions. The USGS maintains a large spectral library composed of mineral and soil types, which has image spectra and can be compared directly. 1.1 Study Area and image data The lithology of the Chitradurga schist belt 13036’25’’N and 760 35’49’’E belongs to both Bababudan and Chitradurga Groups. (Figure 1) The Bababudan Group of rocks represented by metabasalt-quartzite formations and NNW trending synclinal Kibbanahalli BIF formation, wrapping around the Chikkanayakanahalli (CN Halli) gneiss and joining the main CN Halli belt near Dodguni (Radhakrishna, 1967; Srinivasan and Sreenivas, 1975; Seshadri et al., 1981; Ramakrishnan and Vaidynadhan, 2008). Chitradurga Group covers most of the CN Halli schist belt, represented by quartz-sericite-chlorite schist, quartzite, carbonates, Mn formations and BIF overlies Bababudan Group (Devaraju and Anantha Murthy, 1976, 1977). EO-1 Hyperion level 1 radiometric (L1R) product having 242 bands covering CN Halli area acquired on 14 April 2011 was used. The image covers the spectral range of 0.4 to 2.5Â µm at 10 nm bandwidth. However, only 155 of them are calibrated from visible-to-infrared (VNIR) and short wave-infrared (SWIR) regions. (Table 1) (EO-1 User Guide, 2003).