Title
Virtual Dimension Analysis of Hyperspectral Imaging to Characterize A Mining Sample
Date Issued
24 March 2021
Access level
metadata only access
Resource Type
conference paper
Publisher(s)
IEEE Computer Society
Abstract
Virtual Dimension (VD) procedure is used to analyze Hyperspectral Image (HIS) treatment-data in order to estimate the abundance of mineral components of a mining sample. Hyperspectral images coming from reflectance spectra (NIR region) are pre-treated using Standard Normal Variance (SNV) and Minimum Noise Fraction (MNF) methodologies. The endmember components are identified by the simplex growing algorithm (SVG) and after adjusted to the reflectance spectra of reference-databases using Simulated Annealing (SA) methodology. The obtained abundance of minerals of the sample studied is very near to the ones obtained using XRD with a total relative error of 2%.
Volume
2021-March
Language
English
OCDE Knowledge area
Geociencias, Multidisciplinar
Scopus EID
2-s2.0-85112800712
Resource of which it is part
Workshop on Hyperspectral Image and Signal Processing, Evolution in Remote Sensing
ISSN of the container
21586276
ISBN of the container
9781665436014
Conference
11th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing, WHISPERS 2021 Amsterdam 24 March 2021 through 26 March 2021
Source funding
Consejo Nacional de Ciencia, Tecnología e Innovación Tecnológica
Sponsor(s)
L. Chevez y A. Apaza wish to thank to CONCYTEC for finantial support under grants N. 168 and N.167.
Sources of information: Directorio de Producción Científica Scopus