F.J.A. van Ruitenbeek

Rotation-Variant Template Matching for Supervised Hyperspectral Boundary Detection

Edge operators are widely used on gray-level images and are recently improved to work with multispectral and even hyperspectral imagery. The high spectral information content in hyperspectral images allows a detailed description of boundaries and thus a supervised boundary detection. In this letter, we describe a template matching algorithm for the detection of fuzzy and crisp boundaries. For this purpose, the template has a one-dimensional design consisting of two different spectra. This template is matched to a remote sensing image by moving and rotating the template over the image. A statistical spatial and spectral fit of the template is calculated for every position and orientation. Important steps in this approach are the design of a template according to our knowledge of a boundary, and, mainly depending on the template design, the interpretation of the algorithm output. The algorithm has been used for the detection of boundaries between selected mineral assemblages in a hyperspectral image that covers a hydrothermal alteration system. Results show that the algorithm successfully detects the boundaries that had been defined in the templates. In addition, it is shown that rotation of the template in the algorithm reveals information on the type of boundary (crisp or fuzzy) and identifies pixels where only one of the template endmembers is present

Ore–Waste Discrimination in Epithermal Deposits Using Near-Infrared to Short-Wavelength Infrared (NIR-SWIR) Hyperspectral Imagery

Near-infrared (NIR) and short-wavelength infrared (SWIR) hyperspectral imagery can be used to detect certain alteration minerals. At epithermal deposits, the formation of alteration minerals is, in theory, related to the mineralisation of gold and silver. In order to provide foundations for developing sensor-based sorting applications at a mine that exploits such a deposit, it was investigated if NIR-SWIR hyperspectral imagery can be used to distinguish between ore and waste particles by characterising the alteration mineralogy. Maps were produced from the NIR-SWIR hyperspectral images of 827 drill core samples that show mineral occurrences, mineral absorption feature intensities and characteristics of the iron oxide mineralogy. Partial least squares discriminant analysis (PLS-DA) was applied to the information contained in these maps to investigate if this information can be used to discriminate between ore and waste. The results showed that NIR-SWIR hyperspectral imagery could be used to segment a population of waste samples by detecting occurrences of pyrophyllite, dickite and/or illite. This result can be explained by the fact that these minerals are commonly deposited further away from the ore-bearing epithermal veins, while the absence of SWIR-active minerals or detected occurrences of alunite are more closely associated with these structures. The ability to identify waste with NIR-SWIR spectral sensors means there is potential that sensor-based sorting can be used to remove this waste from mineral processing operations. Additional research is still required to assess the economic feasibility of such a sensor-based sorting application.

Applicability of near-infrared hyperspectral imagery (NIR-HI) for sensor based sorting of an epithermal Au-Ag ore

In the presented study test work was performed with near-infrared hyperspectral imagery (NIR-HI) on 36 ore samples from a South-American epithermal Au-Ag mine. The aim of the test work was to investigate if NIR-HI provides information about the alteration mineralogy of samples that can be used to predict their economic value. Mineral distribution maps were produced from the hyperspectral images by using correlation coefficients between the image pixels and a set of reference spectra. These maps showed that detection of mineralogy with NIR-HI can be used to distinguish; i) ore particles with low Au and Ag grades, ii) ore particles with high carbon contents, iii) ore particles with high sulphur contents.