首页>
外文期刊>geocarto international
>Calibration of Airborne Visible/Infrared Imaging Spectrometer Data (AVIRIS) to reflectance and mineral mapping in hydrothermal alteration zones: An example from the #x201C;Cuprite mining district#x201D;
【24h】
Calibration of Airborne Visible/Infrared Imaging Spectrometer Data (AVIRIS) to reflectance and mineral mapping in hydrothermal alteration zones: An example from the #x201C;Cuprite mining district#x201D;
展开▼
机译：Calibration of Airborne Visible/Infrared Imaging Spectrometer Data (AVIRIS) to reflectance and mineral mapping in hydrothermal alteration zones: An example from the #x201C;Cuprite mining district#x201D;
High#x2010;spectral resolution images (AVIRIS) over the Cuprite mining area have been used to evaluate atmospheric calibration algorithms and test several mineral mapping techniques. Four normalization techniques have been applied: (1) the Flat#x2010;Field Method, (2) the Equal Area normalization technique using the Internal Average Reflectance (IAR) Spectrum, (3) the Empirical Line Method, and (4) the Atmospheric Absorption Removal Method (ATREM) which uses a standard empirical atmosphere model. The algorithms have been evaluated in terms of their ability to remove both solar irradiance and atmospheric absorption features, noise, artifacts, and spectral interpretability. Signal#x2010;to#x2010;noise ratio (SNR) has been calculated as the ratio of the mean of the digital number (DN) values (reflectivity)) and the standard deviation of the DN values (noise) for pixel spectra. An empirical relation was found between SNR and number of pixel spectra averaged. Spectral interpretability was evaluated by using the difference spectrum (e.g. laboratory spectrum minus pixel spectrum) for areas with known occurrences of clay minerals. These difference spectra were useful in evaluating the removal of atmospheric features. Mineral mapping was done using (1) a comparison between pixel spectra and laboratory spectra, (2) colour#x2010;composites with bands selected on shoulders and centres of expected absorption features, (3) colour coded spectra, and (4) an indicator kriging techniqure which uses bands on the shoulders and centre of expected absorption features to derive the probability that a pixel belongs to a certain mineral class.
展开▼
