A new artefacts resistant method for automatic lineament extraction using Multi-Hillshade Hierarchic Clustering (MHHC)

Šilhavý, Jakub; Minár, Jozef; Mentlík, Pavel; Sládek, J.

doi:10.1016/j.cageo.2016.03.015

Cited by 28 publications

(9 citation statements)

References 26 publications

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“…5. -Axes of valleys were delineated on basis of DEM using semiautomatic algorithm developed by Šilhavý et al [52]. Firstly, the DEM is resampled from source raster in a specific resolution.…”

Section: Methodsmentioning

confidence: 99%

Dependence of Gully Networks on Faults and Lineaments Networks, Case Study from Hronska Pahorkatina Hill Land

et al. 2017

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Abstract:In this paper we analyse the dependence between gully networks, networks of valleys and faults in the area of Hronska pahorkatina Hill Land. The work is based on the analysis of directions and densities of these three networks in the study area and subunits of lower hierarchical level. The coincidence of all three networks is rare. This scenario occurs when networks of gullies are situated on the bottom of shallow valleys which are conditioned by the presence of faults. More often scenario is only the coincidence of network of valleys and gullies. The last scenario appears in areas with low fault density. The most specific scenario is perpendicularity between network of gullies and network of gullies. Gullies are situated on steep slopes of incised valleys in this case. The last scenario appears the most frequently and was also proven by findings from other studies. We propose three comprehensive explanations of the possible dependence between network of gullies and faults. We also suggest the draft of the possible dependence between network of valleys and gullies.

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Section: Methodsmentioning

confidence: 99%

Dependence of Gully Networks on Faults and Lineaments Networks, Case Study from Hronska Pahorkatina Hill Land

et al. 2017

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“…In this study, the directional gradient, Sobel and Laplacian filters as well as the hillshade and TDR transforms have been selected to demonstrate various feature extraction methods. These operators have been selected as they are commonly applied in lineament detection studies, be it manual or semi-automated, to enhance features prior to detection on a variety of datasets (Smithurst, 1990;Morris, 1991;Maini and Aggarwal, 2009;Airo and Wennerström, 2010;Mallast et al, 2011;Grebby et al, 2012;Hashim et al, 2013;Rahnama and Gloaguen, 2014;Sukumar et al, 2014;Middleton et al, 2015;Mwaniki et al, 2015;Scheiber et al, 2015;Sedrette and Rebaï, 2016;ilhav˝et al, 2016;Thiele et al, 2017;Yeomans et al, 2019). It is worth noting that other methods are available such as the Prewitt and Canny filter, Principal Component Analysis and Hough transforms, among others, which fall out of scope of this study.…”

Section: Filters and Transformsmentioning

confidence: 99%

“…The ability to semi-automate lineament extraction and loop through a range of azimuths has meant that more objective lineament maps can be created compared to manual methods (e.g. Rahnama and Gloaguen, 2014;Middleton et al, 2015;ilhav˝et al, 2016;Yeomans et al, 2019). Therefore, a semi-automated lineament detection method can be desirable and increase the objectivity of the analysis.…”

Section: Introductionmentioning

confidence: 99%

Application of the tilt derivative transform to bathymetric data for structural lineament mapping

Yeomans

Head

Lindsay

2021

Journal of Structural Geology

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High-resolution bathymetry surveys provide an opportunity to analyse local geological structure where onshore areas a ord limited exposure. Semi-automated lineament detection methods are necessary for areas of large coverage where a manual analysis would be subjective and timeconsuming. However, semi-automated approaches are dependent on e ective feature extraction methods to identify genuine lineaments. This study o ers solutions to common problems that can impede processing methods where sharp steps in the seafloor (e.g. palaeocoastlines) are present. Directional gradient, Sobel and Laplacian filters are explored as well as the hillshade and tilt derivative transform for feature extraction prior to applying an object-based image analysis lineament detection approach. The filtered datasets generally perform poorly with a marked improvement when using the hillshade transform. However, we find the azimuth-invariant tilt derivative, which incorporates a convolved vertical derivative, to be the most successful, identifying lineaments in a range of orientations and across a sharp step in the seafloor.

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“…In shadow analysis, it is possible to identify the exact location of the continuous surface in the shadow at a specific time of day [47]. Other applications include extraction of lineament data from DEMs [48][49][50]. Using shaded relief to detect or map landslides was proposed by [51,52].…”

Section: Shaded Reliefmentioning

confidence: 99%

A Method for Extracting Some Key Terrain Features from Shaded Relief of Digital Terrain Models

Syzdykbayev

Karimi

2020

Remote Sensing

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Detection of terrain features (ridges, spurs, cliffs, and peaks) is a basic research topic in digital elevation model (DEM) analysis and is essential for learning about factors that influence terrain surfaces, such as geologic structures and geomorphologic processes. Detection of terrain features based on general geomorphometry is challenging and has a high degree of uncertainty, mostly due to a variety of controlling factors on surface evolution in different regions. Currently, there are different computational techniques for obtaining detailed information about terrain features using DEM analysis. One of the most common techniques is numerically identifying or classifying terrain elements where regional topologies of the land surface are constructed by using DEMs or by combining derivatives of DEM. The main drawbacks of these techniques are that they cannot differentiate between ridges, spurs, and cliffs, or result in a high degree of false positives when detecting spur lines. In this paper, we propose a new method for automatically detecting terrain features such as ridges, spurs, cliffs, and peaks, using shaded relief by controlling altitude and azimuth of illumination sources on both smooth and rough surfaces. In our proposed method, we use edge detection filters based on azimuth angle on shaded relief to identify specific terrain features. Results show that the proposed method performs similar to or in some cases better (when detecting spurs than current terrain features detection methods, such as geomorphon, curvature, and probabilistic methods.

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A new artefacts resistant method for automatic lineament extraction using Multi-Hillshade Hierarchic Clustering (MHHC)

Cited by 28 publications

References 26 publications

Dependence of Gully Networks on Faults and Lineaments Networks, Case Study from Hronska Pahorkatina Hill Land

Dependence of Gully Networks on Faults and Lineaments Networks, Case Study from Hronska Pahorkatina Hill Land

Application of the tilt derivative transform to bathymetric data for structural lineament mapping

A Method for Extracting Some Key Terrain Features from Shaded Relief of Digital Terrain Models

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