Study of Mineral Mapping Techniques using Airborne Hyperspectral Data

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Details

Geology is a science deal with the minerals and rocks majorly. Hyperspectral data is used in the geology domain for mineral identification and mapping. Because of its high spatial as well as high spectral resolution. The main objective is to identify the different minerals on the earth crust using different mineral mapping algorithms such as Spectral Angle Mapper (SAM), Spectral Feature Fitting (SFF) and Mixture Tuned Matched Filtering (MTMF) using AVIRIS-NG data and conclude the better algorithm for mineral mapping. For identification of a different kind of minerals, spectral analysis is performed. Imagery pixel spectrum is matched with predefined or unique (pure) spectra for the mineral detection. SAM algorithm mainly computes the angle between the pixel spectra and reference spectra. The SFF compares the continuum removed spectrum of the image with the continuum removed reference spectrum and operated the least square fitting. MTMF is a combination of two different algorithms and works on the MNF transformed data for estimation of abundance of minerals along with the identification of the false positive. MTMF perform well and gives better results for mineral mapping.

Autorentext

Ronak Jain is presently pursuing PhD from Mohanlal Sukhadia University, Rajasthan. He received his M.Sc.(2015) & M.Sc. Tech.(2016) in Geology from MLSU and Post-Graduate Diploma in Geoinformatics (2017) from Indian Institute of Remote Sensing, ISRO and Faculty of Geo-Information Science & Earth Observation (ITC), University of Twente, Netherlands.

Weitere Informationen

  • Allgemeine Informationen
    • Sprache Englisch
    • Anzahl Seiten 72
    • Herausgeber LAP LAMBERT Academic Publishing
    • Gewicht 125g
    • Untertitel Exploring the potential of AVIRIS-NG for Mineral Identification
    • Autor Ronak Jain , Anil Kumar , Richa U Sharma
    • Titel Study of Mineral Mapping Techniques using Airborne Hyperspectral Data
    • Veröffentlichung 08.02.2018
    • ISBN 6137344266
    • Format Kartonierter Einband (Kt)
    • EAN 9786137344262
    • Jahr 2018
    • Größe H220mm x B150mm x T5mm
    • GTIN 09786137344262

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