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Fast And Easy Mineral Classification Using CASI/SASI/TASI

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00025798%3A_____%2F19%3A00000121" target="_blank" >RIV/00025798:_____/19:00000121 - isvavai.cz</a>

  • Result on the web

    <a href="http://is.earsel.org/workshop/11-IS-Brno2019/" target="_blank" >http://is.earsel.org/workshop/11-IS-Brno2019/</a>

  • DOI - Digital Object Identifier

Alternative languages

  • Result language

    angličtina

  • Original language name

    Fast And Easy Mineral Classification Using CASI/SASI/TASI

  • Original language description

    The poster was presented at the 11th EARSeL SIG IS Workshop in Brno.Abstract: Independent spectral analysis is usually employed to analyse hyperspectral optical (visible: VIS, near infrared: NIR, shortwave infrared: SWIR) and thermal (longwave infrared: LWIR) data. The integration of the spectral information provided by different wavelength ranges and the subsequent complex classification still remains challenging. In this paper we demonstrate the benefits of mineral classification employed to optical and thermal hyperspectral data (CASI and SASI: 0.4-2.5 micrometer; TASI: 8.6-11.5 micrometer) when using new tools (QUANTools) developed at the Czech Geological Survey (CGS). The concept is based on the automatic detection of multiple absorption features; moreover it allows quick data processing and classification without requiring endmember definition prior to spectral mapping. As a result 12 mineral classes were identified integrating together the spectral information from CASI, SASI and TASI imaging data. A representative sample for each mapped class was collected and, consequently, semi-quantitative XRD diffraction analysis was conducted to resolve the mineralogy in further detail. We can conclude that the new concept allows for quick integration and classification of the VIS/NIR, SWIR and LWIR hyperspectral data. The approach can increase time/cost efficiency as the validation samples can be collected after image classification targeting specifically the identified surface variability (e.g., mapped classes).

  • Czech name

  • Czech description

Classification

  • Type

    O - Miscellaneous

  • CEP classification

  • OECD FORD branch

    20705 - Remote sensing

Result continuities

  • Project

    <a href="/en/project/GA17-05743S" target="_blank" >GA17-05743S: New spectral insight into biogeochemistry of small forested watersheds</a><br>

  • Continuities

    P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Others

  • Publication year

    2019

  • Confidentiality

    S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů