Abstract
Increased dimensionality of the satellite data proves to be very useful for discriminating features with very close spectral matching. Present study concentrates on the retrieval of reflectance spectra from the level one radiometrically corrected data in Koraput district (Orissa) for the Bauxite ore. In the present study, atmospheric correction model FLAASH has been used to retrieve reflectance from the radiance data. Preprocessing of the dataset has been done before applying atmospheric correction on the dataset. Spectral subsetting of noise prone bands has been successfully done. Local destriping of the affected bands has been done using a 3*3 local mean filter. Spectral signatures of samples were derived from the processed data. Spectral signature of each sample and derived features vectors were correlated with the satellite image of the area and distribution of each feature was demarcated. Spatial abundance of each feature was used in preparation of mineral abundance map. Accuracy of the map was assessed using training sets of representative geological units. The mineral abundance mapping using the spectral analysis of the reflectance image involves the endmember collection using the N-Dimensional visualizer tool in ENVI software. Laterite, Bauxite, Iron and silica rich Aluminous laterite soil, Alluvium and Forest were selected as the end members after understanding the geology and analysis of the reflectance image. Various mapping techniques were applied to generate the final classified mineral abundance Map, Linear Spectral Unmixing, Mixture Tune Matched Filtering, Spectral Feature Fitting, Spectral Angle Mapper were the techniques used. Results have revealed the ability of Hyper spectral Remote sensing data for the identification and mapping of Hydrothermal altered products like Bauxite, Aluminous Laterite. This technology can be utilized for targeting minerals in the altered zone.
Highlights
One of the most promising and advanced remote sensing technique which is meant solely for mineral exploration is hyperspectral remote sensing known as imaging spectrometry
Linear spectral unmixing has been performed using the final endmembers of the study area
It is obtained by giving endmember region of interest as the reference spectra and their abundance in the image are given in the resultant final Linear spectral unmixing image
Summary
One of the most promising and advanced remote sensing technique which is meant solely for mineral exploration is hyperspectral remote sensing known as imaging spectrometry. Hyperspectral spaceborne imaging spectrometers have been developed to measure the solar reflected upwelling radiance spectrum from 350 nm to 2560 nm at 5 to 10 nm resolution. Hyperspectral imagers collect data in contiguous narrow bands (up to several hundred bands) in the electromagnetic spectrum. They produce vast quantities of data because of the number of bands simultaneously imaged. Hyperspectral data provide unique capabilities to discern physical and chemical properties of Earth surface features which are difficult using current broad-band multi-spectral satellites. High spectral resolution allows identification of materials in the scene, while high spatial resolution locates those materials [1]. Hyperspectral data have enormous potential in target detection, high quality material mapping and identification
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