Measuring rock microstructure in hyperspectral mineral maps

A novel method is presented to measure rock microstructure in hyperspectral mineral maps of rock specimens. Shape parameters were calculated from rock objects in segmented mineral maps. Object area, object perimeter, object hull perimeter and fitted ellipses were used to calculate shape parameters s...

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Veröffentlicht in:Remote sensing of environment 2019-01, Vol.220, p.94-109
Hauptverfasser: van Ruitenbeek, F.J.A., van der Werff, H.M.A., Bakker, W.H., van der Meer, F.D., Hein, K.A.A.
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container_start_page 94
container_title Remote sensing of environment
container_volume 220
creator van Ruitenbeek, F.J.A.
van der Werff, H.M.A.
Bakker, W.H.
van der Meer, F.D.
Hein, K.A.A.
description A novel method is presented to measure rock microstructure in hyperspectral mineral maps of rock specimens. Shape parameters were calculated from rock objects in segmented mineral maps. Object area, object perimeter, object hull perimeter and fitted ellipses were used to calculate shape parameters such as compactness, convexity and a cookie-cutter parameter. Shape parameters were used to describe a variety of microstructures and microstructural elements. The parameters were tested on microstructures in artificial imagery and subsequently applied to hyperspectral mineral maps of rocks. Analyses of parameters calculated on artificial imagery showed that object shapes could be measured by the flattening of fitted ellipses as a measure of sphericity and elongation, together with the cookie-cutter parameters that measured angularity. Compactness and convexity could differentiate between euhedral, subhedral and anhedral crystal shapes. Aphanitic, phaneritic and porphyritic igneous microstructures could be identified and differentiated by homogeneity and relative object size parameters. The degree of sorting of sedimentary rocks was measured by the distribution of object sizes and statistical parameters describing the distribution. Orientation of single objects was measured by the angle between the major axis of a fitted ellipse and the vertical of the image. Preferred orientations in the rock microstructure were determined by calculation of a standardized resultant of orientation vectors and a mean angle. Layering and banding of the rock was identified by the length of major axes of fitted ellipses relative to the image dimension. The shape parameters calculated on objects in segmented hyperspectral mineral maps of rock specimens were able to discriminate between sedimentary and volcanic microstructures using the size distribution of mineral objects, the presence of a preferred orientation of the rock and a layered microstructure. The volcanic microstructures could be differentiated by the size distribution of amygdales, phenocrysts and xenocrysts in the rock. Shape parameters could be used to differentiate between xenocrysts and phenocrysts, the latter being more elongated in the studied samples. The study shows that object shape parameters can be used to measure microstructure and microstructural elements in mineral maps, and subsequently discriminate between different rock types and microstructures. The expression of microstructure into numeric parameters is
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Shape parameters were calculated from rock objects in segmented mineral maps. Object area, object perimeter, object hull perimeter and fitted ellipses were used to calculate shape parameters such as compactness, convexity and a cookie-cutter parameter. Shape parameters were used to describe a variety of microstructures and microstructural elements. The parameters were tested on microstructures in artificial imagery and subsequently applied to hyperspectral mineral maps of rocks. Analyses of parameters calculated on artificial imagery showed that object shapes could be measured by the flattening of fitted ellipses as a measure of sphericity and elongation, together with the cookie-cutter parameters that measured angularity. Compactness and convexity could differentiate between euhedral, subhedral and anhedral crystal shapes. Aphanitic, phaneritic and porphyritic igneous microstructures could be identified and differentiated by homogeneity and relative object size parameters. The degree of sorting of sedimentary rocks was measured by the distribution of object sizes and statistical parameters describing the distribution. Orientation of single objects was measured by the angle between the major axis of a fitted ellipse and the vertical of the image. Preferred orientations in the rock microstructure were determined by calculation of a standardized resultant of orientation vectors and a mean angle. Layering and banding of the rock was identified by the length of major axes of fitted ellipses relative to the image dimension. The shape parameters calculated on objects in segmented hyperspectral mineral maps of rock specimens were able to discriminate between sedimentary and volcanic microstructures using the size distribution of mineral objects, the presence of a preferred orientation of the rock and a layered microstructure. The volcanic microstructures could be differentiated by the size distribution of amygdales, phenocrysts and xenocrysts in the rock. Shape parameters could be used to differentiate between xenocrysts and phenocrysts, the latter being more elongated in the studied samples. The study shows that object shape parameters can be used to measure microstructure and microstructural elements in mineral maps, and subsequently discriminate between different rock types and microstructures. The expression of microstructure into numeric parameters is a first step towards quantification of microstructures in mineral maps of rocks. Further development of the methodology could contribute to the creation of unbiased classification scheme of rocks, improved statistical modeling of compositional rock parameters such as mineral ore grades, and the automated recognition of microstructures in large image databases of rocks and drill-core. [Display omitted] •Rock microstructure in mineral maps was measured using shape parameters.•The shape parameters were calculated on objects in segmented maps.•Methods were developed on artificial imagery of object shapes and microstructures.•Results were applied to hyperspectral mineral maps of rock samples.•Rock microstructures could be differentiated using object shape parameters.</description><identifier>ISSN: 0034-4257</identifier><identifier>EISSN: 1879-0704</identifier><identifier>DOI: 10.1016/j.rse.2018.10.030</identifier><language>eng</language><publisher>New York: Elsevier Inc</publisher><subject>Banding ; Convexity ; Coring ; Ellipses ; Elongation ; Geology ; Homogeneity ; Hyperspectral ; Imagery ; Infrared ; Infrared radiation ; Mathematical models ; Measurement ; Microstructure ; Object recognition ; Parameter identification ; Preferred orientation ; Rock ; Rocks ; Sedimentary rocks ; Shape ; Size distribution ; Statistical analysis ; Statistical methods ; Statistical models ; Texture</subject><ispartof>Remote sensing of environment, 2019-01, Vol.220, p.94-109</ispartof><rights>2018 Elsevier Inc.</rights><rights>Copyright Elsevier BV Jan 2019</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c368t-ca94561c8b22dbc84174e86a72ee3050b9a35835459d6b714ff67402bd97b2313</citedby><cites>FETCH-LOGICAL-c368t-ca94561c8b22dbc84174e86a72ee3050b9a35835459d6b714ff67402bd97b2313</cites><orcidid>0000-0003-2347-1625</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://dx.doi.org/10.1016/j.rse.2018.10.030$$EHTML$$P50$$Gelsevier$$H</linktohtml><link.rule.ids>314,780,784,3550,27924,27925,45995</link.rule.ids></links><search><creatorcontrib>van Ruitenbeek, F.J.A.</creatorcontrib><creatorcontrib>van der Werff, H.M.A.</creatorcontrib><creatorcontrib>Bakker, W.H.</creatorcontrib><creatorcontrib>van der Meer, F.D.</creatorcontrib><creatorcontrib>Hein, K.A.A.</creatorcontrib><title>Measuring rock microstructure in hyperspectral mineral maps</title><title>Remote sensing of environment</title><description>A novel method is presented to measure rock microstructure in hyperspectral mineral maps of rock specimens. Shape parameters were calculated from rock objects in segmented mineral maps. Object area, object perimeter, object hull perimeter and fitted ellipses were used to calculate shape parameters such as compactness, convexity and a cookie-cutter parameter. Shape parameters were used to describe a variety of microstructures and microstructural elements. The parameters were tested on microstructures in artificial imagery and subsequently applied to hyperspectral mineral maps of rocks. Analyses of parameters calculated on artificial imagery showed that object shapes could be measured by the flattening of fitted ellipses as a measure of sphericity and elongation, together with the cookie-cutter parameters that measured angularity. Compactness and convexity could differentiate between euhedral, subhedral and anhedral crystal shapes. Aphanitic, phaneritic and porphyritic igneous microstructures could be identified and differentiated by homogeneity and relative object size parameters. The degree of sorting of sedimentary rocks was measured by the distribution of object sizes and statistical parameters describing the distribution. Orientation of single objects was measured by the angle between the major axis of a fitted ellipse and the vertical of the image. Preferred orientations in the rock microstructure were determined by calculation of a standardized resultant of orientation vectors and a mean angle. Layering and banding of the rock was identified by the length of major axes of fitted ellipses relative to the image dimension. The shape parameters calculated on objects in segmented hyperspectral mineral maps of rock specimens were able to discriminate between sedimentary and volcanic microstructures using the size distribution of mineral objects, the presence of a preferred orientation of the rock and a layered microstructure. The volcanic microstructures could be differentiated by the size distribution of amygdales, phenocrysts and xenocrysts in the rock. Shape parameters could be used to differentiate between xenocrysts and phenocrysts, the latter being more elongated in the studied samples. The study shows that object shape parameters can be used to measure microstructure and microstructural elements in mineral maps, and subsequently discriminate between different rock types and microstructures. The expression of microstructure into numeric parameters is a first step towards quantification of microstructures in mineral maps of rocks. Further development of the methodology could contribute to the creation of unbiased classification scheme of rocks, improved statistical modeling of compositional rock parameters such as mineral ore grades, and the automated recognition of microstructures in large image databases of rocks and drill-core. 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Shape parameters were calculated from rock objects in segmented mineral maps. Object area, object perimeter, object hull perimeter and fitted ellipses were used to calculate shape parameters such as compactness, convexity and a cookie-cutter parameter. Shape parameters were used to describe a variety of microstructures and microstructural elements. The parameters were tested on microstructures in artificial imagery and subsequently applied to hyperspectral mineral maps of rocks. Analyses of parameters calculated on artificial imagery showed that object shapes could be measured by the flattening of fitted ellipses as a measure of sphericity and elongation, together with the cookie-cutter parameters that measured angularity. Compactness and convexity could differentiate between euhedral, subhedral and anhedral crystal shapes. Aphanitic, phaneritic and porphyritic igneous microstructures could be identified and differentiated by homogeneity and relative object size parameters. The degree of sorting of sedimentary rocks was measured by the distribution of object sizes and statistical parameters describing the distribution. Orientation of single objects was measured by the angle between the major axis of a fitted ellipse and the vertical of the image. Preferred orientations in the rock microstructure were determined by calculation of a standardized resultant of orientation vectors and a mean angle. Layering and banding of the rock was identified by the length of major axes of fitted ellipses relative to the image dimension. The shape parameters calculated on objects in segmented hyperspectral mineral maps of rock specimens were able to discriminate between sedimentary and volcanic microstructures using the size distribution of mineral objects, the presence of a preferred orientation of the rock and a layered microstructure. The volcanic microstructures could be differentiated by the size distribution of amygdales, phenocrysts and xenocrysts in the rock. Shape parameters could be used to differentiate between xenocrysts and phenocrysts, the latter being more elongated in the studied samples. The study shows that object shape parameters can be used to measure microstructure and microstructural elements in mineral maps, and subsequently discriminate between different rock types and microstructures. The expression of microstructure into numeric parameters is a first step towards quantification of microstructures in mineral maps of rocks. Further development of the methodology could contribute to the creation of unbiased classification scheme of rocks, improved statistical modeling of compositional rock parameters such as mineral ore grades, and the automated recognition of microstructures in large image databases of rocks and drill-core. [Display omitted] •Rock microstructure in mineral maps was measured using shape parameters.•The shape parameters were calculated on objects in segmented maps.•Methods were developed on artificial imagery of object shapes and microstructures.•Results were applied to hyperspectral mineral maps of rock samples.•Rock microstructures could be differentiated using object shape parameters.</abstract><cop>New York</cop><pub>Elsevier Inc</pub><doi>10.1016/j.rse.2018.10.030</doi><tpages>16</tpages><orcidid>https://orcid.org/0000-0003-2347-1625</orcidid><oa>free_for_read</oa></addata></record>
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subjects Banding
Convexity
Coring
Ellipses
Elongation
Geology
Homogeneity
Hyperspectral
Imagery
Infrared
Infrared radiation
Mathematical models
Measurement
Microstructure
Object recognition
Parameter identification
Preferred orientation
Rock
Rocks
Sedimentary rocks
Shape
Size distribution
Statistical analysis
Statistical methods
Statistical models
Texture
title Measuring rock microstructure in hyperspectral mineral maps
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