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Hyperspectral outcrop models for palaeoseismic studies
Author(s) -
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,
Mattila Jussi,
Nordbäck Nicklas,
Palmu JukkaPekka,
Tiljander Mia,
Ruskeeniemi Timo
Publication year - 2019
Publication title -
the photogrammetric record
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.638
H-Index - 51
eISSN - 1477-9730
pISSN - 0031-868X
DOI - 10.1111/phor.12300
Subject(s) - hyperspectral imaging , geology , outcrop , point cloud , workflow , remote sensing , photogrammetry , visualization , computer science , artificial intelligence , geomorphology , database
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, a new workflow is presented 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.