<abstract xmlns="http://www.w3.org/1999/xhtml"><p> Based on laboratory, field and airborne-acquired hyperspectral data, this paper aims to analyse the dominant minerals and rocks found in the Polish Karkonosze Mountains. Laboratory spectral characteristics were measured with the ASD FieldSpec 3 spectrometer and images were obtained from VITO’s Airborne Prism EXperiment (APEX) scanner. The terrain is covered mainly by lichens or vascular plants creating significant difficulties for rock identification. However, hyperspectral airborne imagery allowed for subpixel classifications of different types of granites, hornfels and mica schist within the research area. The hyperspectral data enabled geological mapping of bare ground that had been masked out using three advanced algorithms: Spectral Angle Mapper, Linear Spectral Unmixing and Matched Filtering. Though all three methods produced positive results, the Matched Filtering method proved to be the most effective. The result of this study was a set of maps and post classification statistical data of rock distribution in the area, one for each method of classification.</p></abstract>

Based on laboratory, field and airborne-acquired hyperspectral data, this paper aims to analyse the dominant minerals and rocks found in the Polish Karkonosze Mountains. Laboratory spectral characteristics were measured with the ASD FieldSpec 3 spectrometer and images were obtained from VITO’s Airborne Prism EXperiment (APEX) scanner. The terrain is covered mainly by lichens or vascular plants creating significant difficulties for rock identification. However, hyperspectral airborne imagery allowed for subpixel classifications of different types of granites, hornfels and mica schist within the research area. The hyperspectral data enabled geological mapping of bare ground that had been masked out using three advanced algorithms: Spectral Angle Mapper, Linear Spectral Unmixing and Matched Filtering. Though all three methods produced positive results, the Matched Filtering method proved to be the most effective. The result of this study was a set of maps and post classification statistical data of rock distribution in the area, one for each method of classification.

Assessment of Imaging Spectroscopy for rock identification in the Karkonosze Mountains, Poland

Department of Geoinformatics, Cartography and Remote Sensing, Faculty of Geography and Regional Studies, University of Warsaw, Poland

Miscellanea Geographica

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Based on laboratory, field and airborne-acquired hyperspectral data, this paper aims to analyse the dominant minerals and rocks found in the Polish Karkonosze...

Assessment of Imaging Spectroscopy for rock identification in the Karkonosze Mountains, Poland

Data publikacji: 20 kwi 2016

Zakres stron: 34 - 40

Otrzymano: 20 maj 2015

Przyjęty: 20 sie 2015

DOI: https://doi.org/10.1515/mgrsd-2015-0016

Słowa kluczowe
Rock identification, Imaging Spectroscopy, APEX hyperspectral airborne imagery data, Spectral Angle Mapper, Linear Spectral Unmixing, Matched Filtering

© by Monika Mierczyk

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Assessment of Imaging Spectroscopy for rock identification in the Karkonosze Mountains, Poland

Data publikacji: 20 kwi 2016

Zakres stron: 34 - 40

Otrzymano: 20 maj 2015

Przyjęty: 20 sie 2015

DOI: https://doi.org/10.1515/mgrsd-2015-0016

Słowa kluczoweRock identification, Imaging Spectroscopy, APEX hyperspectral airborne imagery data, Spectral Angle Mapper, Linear Spectral Unmixing, Matched Filtering

© by Monika Mierczyk

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Słowa kluczowe
Rock identification, Imaging Spectroscopy, APEX hyperspectral airborne imagery data, Spectral Angle Mapper, Linear Spectral Unmixing, Matched Filtering