Stargaze Entertainment Group Inc (STGZ)

[rwa3848]

IT IS OBVIOUS THAT THE PRESIDENT OF UMNG'S 6.12.09 BLOG POST IS TALKING ABOUT THE USE OF HYPERSPECTRAL DATA ALGORITHMS TO RAPIDLY FINGER THE SUBTLE ELECTROMAGNETIC IDENTIFIERS FOR THE PURPOSE OF MINERAL EXPLORATION. THIS TECHNOLOGY HAS CROSS-DOMAIN APPLICATION (IT FINDS LANDMINES, DEAD BODIES, MANMADE POLLUTANTS AND VALUABLE MINERALS USING THE SAME TECHNIQUES)IT IS RIDICULOUS TO SAY A TECHNOLOGY THAT DOES EXIST FOR THE PURPOSES DESCRIBED IN THE PRESIDENT OF UMNG'S BLOG (A TECHNOLOGY FIRST DEVELOPED FOR SATELLITE AND TELESCOPIC EXPLORATION OF THE CHEMICAL COMPOSITON OF THE SOLAR SYSTEM ) DOES NOT EXIST. AIRPLANES AND GROUND VEHICLES (ARE EQUIPPED WITH HYPERSPECTRAL SENSORS FOR THE COMMERCIAL PURPOSES DESCRIBED

Spatial-spectral contextual image analysis of hyperspectral data to aid in the characterisation of hydrothermal alteration in epithermal gold deposits


Hyperspectral remote sensing deals with instruments that sample the EM spectrum at high spectral resolution and with high spectral sampling intervals to produce surface reflectance data that can be readily compared to spectral signatures contained in spectral libraries. In geology, this technology has been used for mineral mapping and geobotanical studies to aid in mineral prospecting, environmental studies and petroleum exploration. Most image processing techniques that are available to analyse hyperspectral data by-pass three important aspects of remote sensing:1.Neighbourhood information is not incorporated and hence the common knowledge formulated in geostatistics that nearby control points are more likely to contain information more similar than far apart control points,2.surface information is not coupled to shallow subsurface information although many problems are in need of a 3 dimensional approach of study 3.and the change over time of the spectral signature of certain earth surface materials that contains information on the type (and use) of the material at hand that may allow a better classification and or discrimination (from other) of materials is not used in sub pixel classification. This paper addresses our efforts in designing spatial-spectral contextual image analysis approaches, thus tackling some of the aspects mentioned under 1. To combine both spectral as well as spatial information in the analysis of hyperspectral imagery we have developed a spatial spectral algorithm; the template matching algorithm. A template consists of a one or two dimensional array that is filled on both sides of a central pixel with spectra (or spectral derivatives such as ratios etc) that characterize certain cover types of interest. The template is matched with an image by moving the kernel or a filter over the image and calculating parameters at each pixel. At each pixel the template is also rotated and a number of parameters are calculated (e.g., best template fit, worst template fit, mean template fit, variance in template fit, and optimal scanangle). Examples are given to the application of template matching as a spatial-spectral contextual image processing technique dealing with the detection of mineral assemblage coexistence in relation to hydrothermal alteration in a epithermal gold deposit.