Filtering of satellite images in geological lineament analyses: An application to a fault zone in Central Turkey

Süzen, Mehmet; Toprak, Vedat

doi:10.1080/014311698215621

Cited by 175 publications

(79 citation statements)

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“…A high-contrast image contains a wide distribution of pixel counts covering the entire amplitude range. A low contrast image has most of the pixel amplitudes congregated in a relatively narrow range (Süzen et al, 1998 andGonzalez andWoods 1992).…”

Section: Histogram Equalizationmentioning

confidence: 99%

“…We can detect all of these conditions in aerial or space imagery (Majumdar and Bhattacharya 1998;Katsuaki et al, 1995;Walsh 2000;Mostafa and Bishta, 2005;Semere and Ghebreab, 2006 Consequently, optical remote sensing techniques over more than three decades have shown a great promise for mapping geological feature variations over wide scale (Mostafa and Bishta, 2005;Semere and Ghebreab, 2006;Marghany et al, 2009a). In referring to Katsuaki et al, (1995);Walsh (2000) lineament information extractions in satellite images can be divided broadly into three categories: (i) lineament enhancement and lineament extraction for characterization of geologic structure;(ii) image classification to perform geologic mapping or to locate spectrally anomalous zones attributable to mineralization (Mostafa et al, 1995;Süzen and Toprak 1998); and (iii) superposition of satellite images and multiple data such as geological, geochemical, and geophysical data in a geographical information system (Novak and Soulakellis 2000;Semere and Ghebreab 2006). Furthermore, remote sensing data assimilation in real time could be a bulk tool for geological features extraction and mapping.…”

Section: Satellite Remote Sensing and Image Processing For Lineament mentioning

confidence: 99%

“…In practice, researchers have preferred to use the spatial domain filtering techniques in order to get ride of the artificial lineaments and to verify disjoint lineament pixels in satellite data (Süzen and Toprak 1998). Further, Leech et al, (2003) implemented the band-rationing, linear and Gaussian nonlinear stretching enhancement techniques to determine lineament populations.…”

Section: Satellite Remote Sensing and Image Processing For Lineament mentioning

confidence: 99%

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Three-Dimensional Lineament Visualization Using Fuzzy B-Spline Algorithm from Multispectral Satellite Data

Marghany¹

2012

Remote Sensing - Advanced Techniques and Platforms

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Section: Histogram Equalizationmentioning

confidence: 99%

Section: Satellite Remote Sensing and Image Processing For Lineament mentioning

confidence: 99%

Section: Satellite Remote Sensing and Image Processing For Lineament mentioning

confidence: 99%

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Three-Dimensional Lineament Visualization Using Fuzzy B-Spline Algorithm from Multispectral Satellite Data

Marghany¹

2012

Remote Sensing - Advanced Techniques and Platforms

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“…Although arguably the most 64 reliable and accurate maps are those produced using this approach, large map 65 areas, time constraints and the limited ground perspective of the field geologist 66 has the potential to increase the possibility that not all structural features will be 67 identified (Süzen and Toprak 1998 area is responsible for a lack of completely exposed outcrops in the study area. 178…”

Section: A Note On Versionsmentioning

confidence: 99%

Application of airborne LiDAR data and airborne multispectral imagery to structural mapping of the upper section of the Troodos ophiolite, Cyprus

Grebby

Cunningham

Naden

et al. 2011

Int J Earth Sci (Geol Rundsch)

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(2012) Application of airborne LiDAR data and airborne multispectral imagery to structural mapping of the upper section of the Troodos ophiolite, Cyprus. International Journal of Earth Sciences, 101 (6). pp. 1645 -1660 . ISSN 1437 Access from the University of Nottingham repository: http://eprints.nottingham.ac.uk/33862/1/Grebby_et_al_2012.pdf Copyright and reuse:The Nottingham ePrints service makes this work by researchers of the University of Nottingham available open access under the following conditions. This article is made available under the University of Nottingham End User licence and may be reused according to the conditions of the licence. For more details see: http://eprints.nottingham.ac.uk/end_user_agreement.pdf A note on versions:The version presented here may differ from the published version or from the version of record. If you wish to cite this item you are advised to consult the publisher's version. Please see the repository url above for details on accessing the published version and note that access may require a subscription. history. In addition, structural maps may inform seismic and landslide hazard 58 assessments, and provide useful information for major engineering projects and 59 the exploration of groundwater, petroleum and mineral resources (Moore and 60 Waltz 1983; Kresic 1995;Karnieli et al. 1996;Wladis 1999; Harris et al. 2001; 61 Peña and Abdelsalam 2006;Corgne et al. 2010). 62Traditionally, structural maps are produced by mapping features such as 63 faults, folds, fabrics, fractures and joints in the field. Although arguably the most 64 reliable and accurate maps are those produced using this approach, large map 65 areas, time constraints and the limited ground perspective of the field geologist 66 has the potential to increase the possibility that not all structural features will be 67 identified (Süzen and Toprak 1998 area is responsible for a lack of completely exposed outcrops in the study area. 178Vegetation cover type generally varies from moderate-to-dense lichen cover, to 179 crops (e.g., cereals, olive groves) as well as both green and dry grasses, to what 180 can be broadly described as garrigue or maquis, predominantly comprising 181 scrubby short dry grasses, short-to-medium height shrubs and scattered small 182 trees. Other types of mostly sporadic vegetation cover occurring throughout the 183 study area include trees -ranging from isolated trees (e.g., pines and oaks) to 184 dense thickets and copses -and areas covered by tall, dry grasses and scrubland. Ltd., Finland). Points corresponding to non-ground returns were subsequently 203 discarded, whilst those classified as ground returns were interpolated using a 204 block kriging algorithm in order to generate a 4 m digital terrain model (DTM) or 205 "bare-earth" DEM (Fig. 1b). A more detailed description of the airborne LiDAR 206 data processing steps is provided by Grebby et al. (2010). 207The ATM imagery initially comprised 11 spectral bands located in the 208 visible/near-infrared (VNIR; Bands 1-8), ...

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“…ASTER GDEM data is useful for surface lineaments detection by using the shaded relief image technique. The study of lineaments has been applied successfully to structural geology studies and their applications such as ore-forming systems, mineral exploration, petroleum, nuclear energy facility sittings (Lalor, 1987;Woodall, 1993Woodall, , 1994O'Driscoll andCampbell, 1997, Mostafa andZakir, 1996;Arlegui andSoriano, 1998,Suzen andToprak, 1998;Zakir and others, 1999). Numbers of process steps applied to interpret the lineaments map, which were finally prepared, Fig (2) in addition to the surface drainage system map, Fig (2.a,b) which was derived from Aster GDEM .Both Lineaments and drainage maps were converted to Density maps using arcview3.2a.…”

Section: Remote Sensing Datamentioning

confidence: 99%

Spatial Data Modeling Based MCE Fuzzy Logic for Petroleum Exploration in part of Say'un-Masilah Basin of Yemen

Mohammed¹,

Javed²,

Alshayef³

2017

International Journal of Environment and Geoinformatics

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GIS Provides spatial and analytical support to assess multicriteria (MCE) methods, which are used to combine data to show areas best fulfilling specific criteria. Petroleum explorations would benefit from an MCE method that is spatial, is flexible for combining heterogeneous data, considers the interpretive nature of the data, is geologically applicable, and is applicable for frontier areas or where little information exists regarding probabilities of the presence of petroleum .This study proposes a GIS-based MCE method for petroleum exploration based on fuzzy logic, which fulfills the previously stated requirements using 9 subcriteria combined to produce a favorability map of potential exploration areas. A case study applied to Sayun Almasilah Basin East Yemen. We conclude that the method can be applied in an exploration setting and, as such, is applicable for other regions of the world.

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Filtering of satellite images in geological lineament analyses: An application to a fault zone in Central Turkey

Cited by 175 publications

References 0 publications

Three-Dimensional Lineament Visualization Using Fuzzy B-Spline Algorithm from Multispectral Satellite Data

Three-Dimensional Lineament Visualization Using Fuzzy B-Spline Algorithm from Multispectral Satellite Data

Application of airborne LiDAR data and airborne multispectral imagery to structural mapping of the upper section of the Troodos ophiolite, Cyprus

Spatial Data Modeling Based MCE Fuzzy Logic for Petroleum Exploration in part of Say'un-Masilah Basin of Yemen

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