
Proceedings Paper
Ore minerals textural characterization by hyperspectral imagingFormat | Member Price | Non-Member Price |
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Paper Abstract
The utilization of hyperspectral detection devices, for natural resources mapping/exploitation through remote sensing
techniques, dates back to the early 1970s. From the first devices utilizing a one-dimensional profile spectrometer,
HyperSpectral Imaging (HSI) devices have been developed. Thus, from specific-customized devices, originally
developed by Governmental Agencies (e.g. NASA, specialized research labs, etc.), a lot of HSI based equipment are
today available at commercial level. Parallel to this huge increase of hyperspectral systems development/manufacturing,
addressed to airborne application, a strong increase also occurred in developing HSI based devices for “ground”
utilization that is sensing units able to play inside a laboratory, a processing plant and/or in an open field. Thanks to this
diffusion more and more applications have been developed and tested in this last years also in the materials sectors. Such
an approach, when successful, is quite challenging being usually reliable, robust and characterised by lower costs if
compared with those usually associated to commonly applied analytical off- and/or on-line analytical approaches. In this
paper such an approach is presented with reference to ore minerals characterization. According to the different phases
and stages of ore minerals and products characterization, and starting from the analyses of the detected hyperspectral
firms, it is possible to derive useful information about mineral flow stream properties and their physical-chemical
attributes. This last aspect can be utilized to define innovative process mineralogy strategies and to implement on-line
procedures at processing level. The present study discusses the effects related to the adoption of different hardware
configurations, the utilization of different logics to perform the analysis and the selection of different algorithms
according to the different characterization, inspection and quality control actions to apply.
Paper Details
Date Published: 19 February 2013
PDF: 16 pages
Proc. SPIE 8655, Image Processing: Algorithms and Systems XI, 865510 (19 February 2013); doi: 10.1117/12.2003054
Published in SPIE Proceedings Vol. 8655:
Image Processing: Algorithms and Systems XI
Karen O. Egiazarian; Sos S. Agaian; Atanas P. Gotchev, Editor(s)
PDF: 16 pages
Proc. SPIE 8655, Image Processing: Algorithms and Systems XI, 865510 (19 February 2013); doi: 10.1117/12.2003054
Show Author Affiliations
Giuseppe Bonifazi, Univ. degli Studi di Roma La Sapienza (Italy)
Nicoletta Picone, Univ. degli Studi di Roma La Sapienza (Italy)
Nicoletta Picone, Univ. degli Studi di Roma La Sapienza (Italy)
Silvia Serranti, Univ. degli Studi di Roma La Sapienza (Italy)
Published in SPIE Proceedings Vol. 8655:
Image Processing: Algorithms and Systems XI
Karen O. Egiazarian; Sos S. Agaian; Atanas P. Gotchev, Editor(s)
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