Integrated Object-Based Image Analysis for semi-automated geological lineament detection in southwest England

Yeomans, Christopher M.; Middleton, Maarit; Shail, Robin K.; Grebby, Stephen; Lusty, Paul A.J.. 2019 Integrated Object-Based Image Analysis for semi-automated geological lineament detection in southwest England. Computers & Geosciences, 123. 137-148. https://doi.org/10.1016/j.cageo.2018.11.005

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Official URL: http://dx.doi.org/10.1016/j.cageo.2018.11.005

Abstract/Summary

Regional lineament detection for mapping of geological structure can provide crucial information for mineral exploration. Manual methods of lineament detection are time consuming, subjective and unreliable. The use of semi-automated methods reduces the subjectivity through applying a standardised method of searching. Object-Based Image Analysis (OBIA) has become a mainstream technique for landcover classification, however, the use of OBIA methods for lineament detection is still relatively under-utilised. The Southwest England region is covered by high-resolution airborne geophysics and LiDAR data that provide an excellent opportunity to demonstrate the power of OBIA methods for lineament detection. Herein, two complementary but stand-alone OBIA methods for lineament detection are presented which both enable semi-automatic regional lineament mapping. Furthermore, these methods have been developed to integrate multiple datasets to create a composite lineament network. The top-down method uses threshold segmentation and sub-levels to create objects, whereas the bottom-up method segments the whole image before merging objects and refining these through a border assessment. Overall lineament lengths are longest when using the top-down method which also provides detailed metadata on the source dataset of the lineament. The bottom-up method is more objective and computationally efficient and only requires user knowledge to classify lineaments into major and minor groups. Both OBIA methods create a similar network of lineaments indicating that semi-automatic techniques are robust and consistent. The integration of multiple datasets from different types of spatial data to create a comprehensive, composite lineament network is an important development and demonstrates the suitability of OBIA methods for enhancing lineament detection.

Item Type:	Publication - Article
Digital Object Identifier (DOI):	https://doi.org/10.1016/j.cageo.2018.11.005
ISSN:	00983004
Date made live:	16 Apr 2019 13:29 +0 (UTC)
URI:	https://nora.nerc.ac.uk/id/eprint/522867

Item Type:

Publication - Article

Digital Object Identifier (DOI):

https://doi.org/10.1016/j.cageo.2018.11.005

ISSN:

00983004

Date made live:

16 Apr 2019 13:29 +0 (UTC)

URI:

https://nora.nerc.ac.uk/id/eprint/522867