Detecting of Lithological Units by Using Terrestrial Spectral Data and Remote Sensing Image

Abstract

The objective of the study was to carry out an automatic classification of the lithological units of interest using the integration of remote sensing image, in which various objects are spread on, and terrestrial spectral measurement data. Only endmembers of interest are classified using spectral classification methods such as Spectral Angle Mapper. Following the identification of the types of rock and minerals, integration of remote sensing images and spectral measurement data enable spectral classification. In this study, Short Wave Infrared detector images of Advanced Spaceborne Thermal Emission and Reflection Radiometer satellite and spectroradiometer measurements were used. The study area, Golova with its geological diversity is located in the Kelkit Valley section of the North Anatolian Fault Zone in Northeast of Turkey. Seventeen rock samples were collected and their coordinates were recorded. The samples were categorized via spectral measurements on their thin sections through petrographic analyses. Marble and Meta lava with different lithological were selected as endmembers. SAM was used as the classification method that enables the analysis of the endmember with the threshold value of 0.009 radian for marble and 0.010 radian for metalava. SAM analysis was compared by visual analysis to principle component analysis, decorrelation stretch, band ratio (R: 4/7, G: 4/1, B (2/3) x (4/3)) and band combination analysis (R: 9, G: 4 and B: 5). This study demonstrates that the SAM method can be successfully used in both the integration of remote sensing image and terrestrial spectral measurement data in lithological classification. Both the endmembers of metalava and marbles were detected in the SAM results at the GPS coordinates noted down whilst collecting the rock samples for accuracy assessment.