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<gco:CharacterString>Supplement to: Kirsch, Moritz; Lorenz, Sandra; Zimmermann, Robert; Andreani, Louis; Tusa, Laura; Pospiech, Solveig; Jackisch, Robert; Khodadadzadeh, Mahdi; Ghamisi, Pedram; Unger, Gabriel; Hödl, Philip; Gloaguen, Richard; Middleton, Maarit; Sutinen, Raimo; Ojala, Antti E K; Mattila, Jussi; Nordbäck, Nicklas; Palmu, Jukka-Pekka; Tiljander, Mia; Ruskeeniemi, Timo (2019): Hyperspectral outcrop models for palaeoseismic studies. Photogrammetric Record, 34(168), 385-407, https://doi.org/10.1111/phor.12300</gco:CharacterString>
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<gco:CharacterString>The traditional study of palaeoseismic trenches involving logging, stratigraphic and structural interpretation can be time-consuming and affected by biases and inaccuracies. To overcome these limitations, we present a new workflow that integrates infrared hyperspectral and photogrammetric data to support field-based palaeoseismic observations. As a case study, this method is applied on two palaeoseismic trenches excavated across a post-glacial fault scarp in northern Finnish Lapland. The hyperspectral imagery (HSI) is geometrically and radiometrically corrected, processed using established image processing algorithms and machine learning approaches, and co-registered to a Structure-from-Motion point cloud. HSI-enhanced virtual outcrop models are a useful complement to palaeoseismic field studies as they not only provide an intuitive visualisation of the outcrop and a versatile data archive, but also enable an unbiased assessment of the mineralogical composition of lithologic units and a semi-automatic delineation of contacts and deformational structures in a 3D virtual environment.

Uploaded data:
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