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Geological mapping in Western Tasmania using radar and random forests

Radford, DDC, Cracknell, MJ ORCID: 0000-0001-9843-8251, Roach, MJ ORCID: 0000-0001-8085-7693 and Cumming, GV 2018 , 'Geological mapping in Western Tasmania using radar and random forests' , IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 11, no. 9 , pp. 3075-3087 , doi: 10.1109/JSTARS.2018.2855207.

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Mineral exploration and geological mapping of highly prospective areas in western Tasmania, southern Australia, is challenging due to steep topography, dense vegetation, and limited outcrop. Synthetic aperture radar (SAR) can potentially penetrate vegetation canopies and assist geological mapping in this environment. This study applies manual and automated lithological classification methods to airborne polarimetric TopSAR and geophysical data in the Heazlewood region, western Tasmania. Major discrepancies between classification results and the existing geological map generated fieldwork targets that led to the discovery of previously unmapped rock units. Manual analysis of radar image texture was essential for the identification of lithological boundaries. Automated pixel-based classification of radar data using Random Forests achieved poor results despite the inclusion of textural information derived from gray level co-occurrence matrices. This is because the majority of manually identified features within the radar imagery result from geobotanical and geomorphological relationships, rather than direct imaging of surficial lithological variations. Inconsistent relationships between geology and vegetation or geology and topography limit the reliability of TopSAR interpretations for geological mapping in this environment. However, Random Forest classifications, based on geophysical data and validated against manual interpretations, were accurate (∼90%) even when using limited training data (∼0.15% of total data). These classifications identified a previously unmapped region of mafic–ultramafic rocks, the presence of which was verified through fieldwork. This study validates the application of machine learning for geological mapping in remote and inaccessible localities but also highlights the limitations of SAR data in thickly vegetated terrain.

Item Type: Article
Authors/Creators:Radford, DDC and Cracknell, MJ and Roach, MJ and Cumming, GV
Keywords: airborne geophysics, AirSAR, geological mapping, gray-level co-occurrence matrices (GLCM), Python, radar imaging, Random Forests, remote sensing, scikit-learn, supervised machine learning, synthetic aperture radar (SAR), Tasmania, texture, TopSAR
Journal or Publication Title: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Publisher: Institute of Electrical and Electronics Engineers
ISSN: 1939-1404
DOI / ID Number: 10.1109/JSTARS.2018.2855207
Copyright Information:

© 2018 IEEE

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