Application of spatial classification rules for remotely sensed images
In this paper the remote sensed image classification example using spacial classification rule with distance (SCRD) is examined. This supervised classification method was first presented in paper [11]. This method is improved version of earlier method PBDF [4, 10, 9], during the classification it in...
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Gespeichert in:
| Autor*in: |
Giedrius Stabingis [verfasserIn] Lijana Stabingienė [verfasserIn] |
|---|
| Format: |
E-Artikel |
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| Sprache: |
Englisch ; Litauisch |
| Erschienen: |
2014 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
In: Lietuvos Matematikos Rinkinys - Vilnius University Press, 2020, 55(2014), A |
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| Übergeordnetes Werk: |
volume:55 ; year:2014 ; number:A |
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Link aufrufen |
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| DOI / URN: |
10.15388/LMR.B.2014.12 |
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| 520 | |a In this paper the remote sensed image classification example using spacial classification rule with distance (SCRD) is examined. This supervised classification method was first presented in paper [11]. This method is improved version of earlier method PBDF [4, 10, 9], during the classification it incorporates more spatial information. The advantage of this method is its ability to classify data which is corrupted by Gaussian random field and it is typical to remotely sensed images classified in this letter which are corrupted by clouds. Classification accuracy is compared with earlier method and with other commonly used supervised classification methods. | ||
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10.15388/LMR.B.2014.12 doi (DE-627)DOAJ047980419 (DE-599)DOAJ93089bc44d514e669fd550efb91663a2 DE-627 ger DE-627 rakwb eng lit QA1-939 Giedrius Stabingis verfasserin aut Application of spatial classification rules for remotely sensed images 2014 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In this paper the remote sensed image classification example using spacial classification rule with distance (SCRD) is examined. This supervised classification method was first presented in paper [11]. This method is improved version of earlier method PBDF [4, 10, 9], during the classification it incorporates more spatial information. The advantage of this method is its ability to classify data which is corrupted by Gaussian random field and it is typical to remotely sensed images classified in this letter which are corrupted by clouds. Classification accuracy is compared with earlier method and with other commonly used supervised classification methods. image classification spatial classification rules supervised classification Mathematics Lijana Stabingienė verfasserin aut In Lietuvos Matematikos Rinkinys Vilnius University Press, 2020 55(2014), A (DE-627)1760609579 2335898X nnns volume:55 year:2014 number:A https://doi.org/10.15388/LMR.B.2014.12 kostenfrei https://doaj.org/article/93089bc44d514e669fd550efb91663a2 kostenfrei https://www.journals.vu.lt/LMR/article/view/14914 kostenfrei https://doaj.org/toc/0132-2818 Journal toc kostenfrei https://doaj.org/toc/2335-898X Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ AR 55 2014 A |
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10.15388/LMR.B.2014.12 doi (DE-627)DOAJ047980419 (DE-599)DOAJ93089bc44d514e669fd550efb91663a2 DE-627 ger DE-627 rakwb eng lit QA1-939 Giedrius Stabingis verfasserin aut Application of spatial classification rules for remotely sensed images 2014 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In this paper the remote sensed image classification example using spacial classification rule with distance (SCRD) is examined. This supervised classification method was first presented in paper [11]. This method is improved version of earlier method PBDF [4, 10, 9], during the classification it incorporates more spatial information. The advantage of this method is its ability to classify data which is corrupted by Gaussian random field and it is typical to remotely sensed images classified in this letter which are corrupted by clouds. Classification accuracy is compared with earlier method and with other commonly used supervised classification methods. image classification spatial classification rules supervised classification Mathematics Lijana Stabingienė verfasserin aut In Lietuvos Matematikos Rinkinys Vilnius University Press, 2020 55(2014), A (DE-627)1760609579 2335898X nnns volume:55 year:2014 number:A https://doi.org/10.15388/LMR.B.2014.12 kostenfrei https://doaj.org/article/93089bc44d514e669fd550efb91663a2 kostenfrei https://www.journals.vu.lt/LMR/article/view/14914 kostenfrei https://doaj.org/toc/0132-2818 Journal toc kostenfrei https://doaj.org/toc/2335-898X Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ AR 55 2014 A |
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In this paper the remote sensed image classification example using spacial classification rule with distance (SCRD) is examined. This supervised classification method was first presented in paper [11]. This method is improved version of earlier method PBDF [4, 10, 9], during the classification it incorporates more spatial information. The advantage of this method is its ability to classify data which is corrupted by Gaussian random field and it is typical to remotely sensed images classified in this letter which are corrupted by clouds. Classification accuracy is compared with earlier method and with other commonly used supervised classification methods. |
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In this paper the remote sensed image classification example using spacial classification rule with distance (SCRD) is examined. This supervised classification method was first presented in paper [11]. This method is improved version of earlier method PBDF [4, 10, 9], during the classification it incorporates more spatial information. The advantage of this method is its ability to classify data which is corrupted by Gaussian random field and it is typical to remotely sensed images classified in this letter which are corrupted by clouds. Classification accuracy is compared with earlier method and with other commonly used supervised classification methods. |
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In this paper the remote sensed image classification example using spacial classification rule with distance (SCRD) is examined. This supervised classification method was first presented in paper [11]. This method is improved version of earlier method PBDF [4, 10, 9], during the classification it incorporates more spatial information. The advantage of this method is its ability to classify data which is corrupted by Gaussian random field and it is typical to remotely sensed images classified in this letter which are corrupted by clouds. Classification accuracy is compared with earlier method and with other commonly used supervised classification methods. |
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<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">DOAJ047980419</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230308131705.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">230227s2014 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.15388/LMR.B.2014.12</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)DOAJ047980419</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)DOAJ93089bc44d514e669fd550efb91663a2</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield><subfield code="a">lit</subfield></datafield><datafield tag="050" ind1=" " ind2="0"><subfield code="a">QA1-939</subfield></datafield><datafield tag="100" ind1="0" ind2=" "><subfield code="a">Giedrius Stabingis</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Application of spatial classification rules for remotely sensed images</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2014</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">In this paper the remote sensed image classification example using spacial classification rule with distance (SCRD) is examined. This supervised classification method was first presented in paper [11]. This method is improved version of earlier method PBDF [4, 10, 9], during the classification it incorporates more spatial information. The advantage of this method is its ability to classify data which is corrupted by Gaussian random field and it is typical to remotely sensed images classified in this letter which are corrupted by clouds. Classification accuracy is compared with earlier method and with other commonly used supervised classification methods.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">image classification</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">spatial classification rules</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">supervised classification</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Mathematics</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Lijana Stabingienė</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">In</subfield><subfield code="t">Lietuvos Matematikos Rinkinys</subfield><subfield code="d">Vilnius University Press, 2020</subfield><subfield code="g">55(2014), A</subfield><subfield code="w">(DE-627)1760609579</subfield><subfield code="x">2335898X</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:55</subfield><subfield code="g">year:2014</subfield><subfield code="g">number:A</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.15388/LMR.B.2014.12</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doaj.org/article/93089bc44d514e669fd550efb91663a2</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://www.journals.vu.lt/LMR/article/view/14914</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="u">https://doaj.org/toc/0132-2818</subfield><subfield code="y">Journal toc</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="u">https://doaj.org/toc/2335-898X</subfield><subfield code="y">Journal toc</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_DOAJ</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">55</subfield><subfield code="j">2014</subfield><subfield code="e">A</subfield></datafield></record></collection>
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