Vol. 13, Special Issue 1: 34th EARSeL Symposium, 95-99, 2014

Mineral mapping based on automatic detection of multiple absorption features
Veronika Kopačková, and Lucie Koucká

In comparison with multispectral image data, hyperspectral (HS) imagery with higher spectral resolution provides sufficient spectral resolution to describe diagnostic absorption signatures. Specific chemical bonds in materials, whether solid, liquid or gas, determine the surface reflectance and emittance, as variations in material composition often cause shifts in the position and shape of absorption bands in the spectrum. In addition, detecting the exact absorption wavelength position is a key factor not only for mineral identification but also for tracking diverse environmental processes and pathways. A couple of techniques to identify absorption parameters have been suggested, however, they are capable of a quantitative characterization of just one major absorption feature and, due to the techniques they use, limited to the SWIR region.

In this paper, new tools are presented allowing automatic detection of multiple absorption feature parameters (absorption maximum wavelengths and depths). The tools employ methods based on a trend analysis of the spectra, while bad bands (e. g., noise, error bands) are detected at first and excluded from further analysis. The tools - e.g. called QUANTools - have been created using IDL programming language and can be used under ENVI/IDL (version 5.0 and higher). As multiple absorption features are detected within the VNIR/SWIR/TIR regions and their wavelength positions, respectively, the newly suggested method has a potential to become a new mapping technique suitable for environments with high heterogeneity and dynamics.

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DOI: 10.12760/02-2014-1-17

Submitted: 01 Apr 2014
Revised: 09 Nov 2014
Accepted: 10 Nov 2014
Published: 25 Nov 2014
Responsible editor: Bogdan Zagajewski

Kopčková V & L Koucká, 2014. Mineral mapping based on automatic detection of multiple absorption features. EARSeL eProceedings, 13(S1): 95-99

EARSeL European Association of Remote Sensing Laboratories, Strasbourg, France


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ISSN 1729-3782