Comparison of Landsat-8, ASTER and Sentinel 1 satellite remote sensing data in automatic lineaments extraction: A case study of Sidi Flah-Bouskour inlier, Moroccan Anti Atlas

Adiri, Zakaria; Harti, Abderrazak El; Jellouli, Amine; Lhissou, Rachid; Maacha, Lhou; Azmi, Mohamed; Zouhair, Mohamed; Bachaoui, El Mostafa

doi:10.1016/j.asr.2017.09.006

Cited by 151 publications

(78 citation statements)

References 37 publications

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“…The resulting curves generate polylines if they fall within the tolerance defined by the line fitting error. Finally, two polylines in the simplest case will bind to form a lineament if their two end points form an angle respecting the value specified by the angular difference and the distance by the linking distance [118]. According to the spatial resolution of the OLI imagery, streams are the most important cultural linear features which can be mistaken for geological linear features.…”

Section: Computer Vision-based Framework For Semi-automated Extracmentioning

confidence: 99%

“…The broader spread of extracted lineament data using both filters and the manual photointerpretation compared to those extracted using the GSWA map is attributed to the short length of the extracted geological lineaments ( Fig. 9) [118]. The Canny edge detection and pixel connectivity-edge linking algorithms have been widely used in different fields [11], [94], [125], [126].…”

Section: A Evaluation Of Semi-automated Computer Vision Frameworkmentioning

confidence: 99%

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Computer vision-based framework for extracting tectonic lineaments from optical remote sensing data

Farahbakhsh

Chandra

Olierook

et al. 2019

International Journal of Remote Sensing

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The extraction of geological lineaments from digital satellite data is a fundamental application in remote sensing. The location of geological lineaments such as faults and dykes are of interest for a range of applications, particularly because of their association with hydrothermal mineralization. Although a wide range of applications have utilized computer vision techniques, a standard workflow for application of these techniques to mineral exploration is lacking. We present a framework for extracting geological lineaments using computer vision techniques which is a combination of edge detection and line extraction algorithms for extracting geological lineaments using optical remote sensing data. It features ancillary computer vision techniques for reducing data dimensionality, removing noise and enhancing the expression of lineaments. We test the proposed framework on Landsat 8 data of a mineral-rich portion of the Gascoyne Province in Western Australia using different dimension reduction techniques and convolutional filters. To validate the results, the extracted lineaments are compared to our manual photointerpretation and geologically mapped structures by the Geological Survey of Western Australia (GSWA). The results show that the best correlation between our extracted geological lineaments and the GSWA geological lineament map is achieved by applying a minimum noise fraction transformation and a Laplacian filter. Application of a directional filter instead shows a stronger correlation with the output of our manual photointerpretation and known sites of hydrothermal mineralization. Hence, our framework using either filter can be used for mineral prospectivity mapping in other regions where faults are exposed and observable in optical remote sensing data.

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Section: Computer Vision-based Framework For Semi-automated Extracmentioning

confidence: 99%

Section: A Evaluation Of Semi-automated Computer Vision Frameworkmentioning

confidence: 99%

Computer vision-based framework for extracting tectonic lineaments from optical remote sensing data

Farahbakhsh

Chandra

Olierook

et al. 2019

International Journal of Remote Sensing

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“…In this study, the manual extraction by visual interpretation was adopted for the lineaments identification in the study area [18,68,69,70,71,72,73]. Those lineaments are materialized by the limits formed by the dark and clear zones lines [74][75] more or less regularized and the hydrographic network disturbances [76].Their signature in the field is very diverse, they may correspond to contacts between formations of different lithology, outcrop limits or to fractures or faults lines [77][78].…”

Section: Fig6 Flowchart Of Adopted Methodology For Lineaments Extramentioning

confidence: 99%

“…The remote sensing with its possibilities of synoptic view, represent a key tool for lineaments detection [7][8]. The use of passive remote sensing has been described in morocco by many authors in different geographical contexts [9,10,11,12,13,14,15,16,17,18]. However, this technique remain strongly influenced by atmospheric conditions which affect the quality of acquired images and make hard the lineaments detection, in contrast with radar images -independent of atmospheric conditions [19]-which provide important informations in structural mapping.…”

Section: Introductionmentioning

confidence: 99%

Contribution of gravity data and Sentinel-1 image for structural mapping. Case of Beni Mellal Atlas and Beni Moussa plain (Morocco).

et al. 2018

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Abstract. The present work is a combined study of gravity and Sentine-1 data for fracture mapping in the karstic massif of Beni Mellal Atlas and the adjacent plain of Beni Moussa. In order to locate the various faults that contribute to the study area structuring, the gravimetric contacts analysis method, based on the joint use of the horizontal gradient and the upward continuation at different altitudes, has been applied to the gravity data. To optimize the structural mapping in the study area, the gravimetric lineaments obtained were completed and correlated with the lineaments got from Sentinel-1 image. Four faults families of NE-SW; E-O; N-S and NW-SE directions have been highlighted. There fault families are perfectly combined with the studied area's surface water network, moreover, they corroborate with the previous geological and structural studies.

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“…For rocks, these spectra vary with several factors; After (Abdelmalik and Abd-Allah, 2017); rock type, thickness, geometry, isotropism, metamorphism and weathering are the main factors controlling the reflection behaviour of these rocks. Thus, to highlight the spectral characteristics of each rock, several studies (Adiri et al, 2017;Amri et al, 2017;Asadzadeh and de Souza Filho, 2016;BinamMandeng et al, 2018;Kumar et al, 2015;Ninomiya and Fu, 2016;Pour and Hashim, 2012;Rajendranand Nasir, 2017) have used many enhancement treatments on Landsat and/or Aster images. Many authors reviewed various methods and various spectral processing for geological mapping e.g.…”

Section: Introductionmentioning

confidence: 99%

The Contribution of Sentinel-2 Satellite Images for Geological Mapping in the South of Tafilalet Basin (Eastern Anti-Atlas, Morocco)

Fal

Maanan

Baïdder

et al. 2019

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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<p><strong>Abstract.</strong> Geological mapping in desert, mountainous or densely vegetated areas are sometimes faced with many constraints. Recently several remote sensing methods are used on ASTER or LANDSAT imagery for making that task easier. The aim of this paper is to evaluate the applicability of some of these methods on Sentinel-2A images. The study, therefore, focuses on a lithological classification using these multispectral images in the south of the Tafilalet basin. To achieve this goal, two L1C level images were used. Decorelation stretch combined with the optimal index factor (OIF) and Minimum Noise Fraction (MNF) were the main improvements used for RGB combination images. The classifiers Spectral Angle Mapper (SAM) and the Maximum Likelihood classifier (MLC) have been evaluated for a most accurate classification to be used for our lithofacies mapping. The latest drawn geological maps and RGB images of false colour combinations were used to select regions of interest (ROI) as the endmembers to use for these classifiers. Obtained results showed a clear discrimination of the different lithological units of the study area. Classifications evaluation showed that the Maximum likelihood classifier is more accurate with an overall accuracy of 76% and a Kappa coefficient is 0.74. Finally, this study has shown the importance of the use of sentinel-2 multispectral images in geological mapping and has shown that the high spectral resolution of the VNIR and SWIR bands creates a synergy with the high spatial resolution for optimal lithological mapping.</p>

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Comparison of Landsat-8, ASTER and Sentinel 1 satellite remote sensing data in automatic lineaments extraction: A case study of Sidi Flah-Bouskour inlier, Moroccan Anti Atlas

Cited by 151 publications

References 37 publications

Computer vision-based framework for extracting tectonic lineaments from optical remote sensing data

Computer vision-based framework for extracting tectonic lineaments from optical remote sensing data

Contribution of gravity data and Sentinel-1 image for structural mapping. Case of Beni Mellal Atlas and Beni Moussa plain (Morocco).

The Contribution of Sentinel-2 Satellite Images for Geological Mapping in the South of Tafilalet Basin (Eastern Anti-Atlas, Morocco)

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