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 |
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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 |
doi_str_mv | 10.1016/j.rse.2018.10.030 |
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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.
[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><subject>Banding</subject><subject>Convexity</subject><subject>Coring</subject><subject>Ellipses</subject><subject>Elongation</subject><subject>Geology</subject><subject>Homogeneity</subject><subject>Hyperspectral</subject><subject>Imagery</subject><subject>Infrared</subject><subject>Infrared radiation</subject><subject>Mathematical models</subject><subject>Measurement</subject><subject>Microstructure</subject><subject>Object recognition</subject><subject>Parameter identification</subject><subject>Preferred orientation</subject><subject>Rock</subject><subject>Rocks</subject><subject>Sedimentary rocks</subject><subject>Shape</subject><subject>Size distribution</subject><subject>Statistical analysis</subject><subject>Statistical methods</subject><subject>Statistical models</subject><subject>Texture</subject><issn>0034-4257</issn><issn>1879-0704</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2019</creationdate><recordtype>article</recordtype><recordid>eNp9UMtOwzAQtBBIlMIHcIvEOcHrR-yoJ1Txkoq4wNlynA04tEmwEyT-Hpdy5jTa3ZndnSHkEmgBFMrrrggRC0ZBp7qgnB6RBWhV5VRRcUwWlHKRCybVKTmLsaMUpFawIKsntHEOvn_LwuA-sp13YYhTmN00B8x8n71_jxjiiG4KdpvmPf6iHeM5OWntNuLFHy7J693ty_oh3zzfP65vNrnjpZ5yZyshS3C6ZqypnRagBOrSKobIqaR1ZbnUXApZNWWtQLRtqQRldVOpmnHgS3J12DuG4XPGOJlumEOfThoGUgkFUlaJBQfW3kAM2Jox-J0N3wao2WdkOpMyMvuM9q2UUdKsDhpM7395DCY6j73Dxodk2DSD_0f9A7Vxbkc</recordid><startdate>201901</startdate><enddate>201901</enddate><creator>van Ruitenbeek, F.J.A.</creator><creator>van der Werff, H.M.A.</creator><creator>Bakker, W.H.</creator><creator>van der Meer, F.D.</creator><creator>Hein, K.A.A.</creator><general>Elsevier Inc</general><general>Elsevier BV</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7QF</scope><scope>7QO</scope><scope>7QQ</scope><scope>7SC</scope><scope>7SE</scope><scope>7SN</scope><scope>7SP</scope><scope>7SR</scope><scope>7TA</scope><scope>7TB</scope><scope>7TG</scope><scope>7U5</scope><scope>8BQ</scope><scope>8FD</scope><scope>C1K</scope><scope>F28</scope><scope>FR3</scope><scope>H8D</scope><scope>H8G</scope><scope>JG9</scope><scope>JQ2</scope><scope>KL.</scope><scope>KR7</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><scope>P64</scope><orcidid>https://orcid.org/0000-0003-2347-1625</orcidid></search><sort><creationdate>201901</creationdate><title>Measuring rock microstructure in hyperspectral mineral maps</title><author>van Ruitenbeek, F.J.A. ; van der Werff, H.M.A. ; Bakker, W.H. ; van der Meer, F.D. ; Hein, K.A.A.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c368t-ca94561c8b22dbc84174e86a72ee3050b9a35835459d6b714ff67402bd97b2313</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2019</creationdate><topic>Banding</topic><topic>Convexity</topic><topic>Coring</topic><topic>Ellipses</topic><topic>Elongation</topic><topic>Geology</topic><topic>Homogeneity</topic><topic>Hyperspectral</topic><topic>Imagery</topic><topic>Infrared</topic><topic>Infrared radiation</topic><topic>Mathematical models</topic><topic>Measurement</topic><topic>Microstructure</topic><topic>Object recognition</topic><topic>Parameter identification</topic><topic>Preferred orientation</topic><topic>Rock</topic><topic>Rocks</topic><topic>Sedimentary rocks</topic><topic>Shape</topic><topic>Size distribution</topic><topic>Statistical analysis</topic><topic>Statistical methods</topic><topic>Statistical models</topic><topic>Texture</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><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><collection>CrossRef</collection><collection>Aluminium Industry Abstracts</collection><collection>Biotechnology Research Abstracts</collection><collection>Ceramic Abstracts</collection><collection>Computer and Information Systems Abstracts</collection><collection>Corrosion Abstracts</collection><collection>Ecology Abstracts</collection><collection>Electronics & Communications Abstracts</collection><collection>Engineered Materials Abstracts</collection><collection>Materials Business File</collection><collection>Mechanical & Transportation Engineering Abstracts</collection><collection>Meteorological & Geoastrophysical Abstracts</collection><collection>Solid State and Superconductivity Abstracts</collection><collection>METADEX</collection><collection>Technology Research Database</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ANTE: Abstracts in New Technology & Engineering</collection><collection>Engineering Research Database</collection><collection>Aerospace Database</collection><collection>Copper Technical Reference Library</collection><collection>Materials Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Meteorological & Geoastrophysical Abstracts - Academic</collection><collection>Civil Engineering Abstracts</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><collection>Biotechnology and BioEngineering Abstracts</collection><jtitle>Remote sensing of environment</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>van Ruitenbeek, F.J.A.</au><au>van der Werff, H.M.A.</au><au>Bakker, W.H.</au><au>van der Meer, F.D.</au><au>Hein, K.A.A.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Measuring rock microstructure in hyperspectral mineral maps</atitle><jtitle>Remote sensing of environment</jtitle><date>2019-01</date><risdate>2019</risdate><volume>220</volume><spage>94</spage><epage>109</epage><pages>94-109</pages><issn>0034-4257</issn><eissn>1879-0704</eissn><abstract>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.
[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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