Adaptive pixel unmixing based on a fuzzy ARTMAP neural network with selective endmembers
Abstract Pixel unmixing is essential for the reliable description of many land-cover patterns with low spatial resolution. The fuzzy ARTMAP neural network-based model has been proven effective for pixel unmixing in the literature. In most cases, the forms of the endmember combination in diverse pixe...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Wu, Ke [verfasserIn] Wei, Lifei [verfasserIn] Wang, Xianmin [verfasserIn] Niu, Ruiqing [verfasserIn] |
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E-Artikel |
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Englisch |
| Erschienen: |
2015 |
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Enthalten in: Soft Computing - Springer-Verlag, 2003, 20(2015), 12 vom: 08. Mai, Seite 4723-4732 |
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volume:20 ; year:2015 ; number:12 ; day:08 ; month:05 ; pages:4723-4732 |
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10.1007/s00500-015-1700-y |
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| 520 | |a Abstract Pixel unmixing is essential for the reliable description of many land-cover patterns with low spatial resolution. The fuzzy ARTMAP neural network-based model has been proven effective for pixel unmixing in the literature. In most cases, the forms of the endmember combination in diverse pixels are very distinct. However, traditional fuzzy ARTMAP model neglects such difference and models endmembers as fixed composition entities. Due to this limitation, the mixture model is unable to precisely and effectively represent details in the result image. In this work, we address this issue by applying a new selective endmember spectral mixture model based on fuzzy ARTMAP neural network. We first consider the endmember variability and identify the most suitable form of the endmember combination, and then the fuzzy ARTMAP model is used to perform the unmixing work. Through two experiments, we show that a more accurate endmember combination in the parent pixel results in an adaptive representation image. The results confirm that the proposed algorithm can effectively improve the accuracy of the unmixing results compared with the linear unmixing method and the traditional fuzzy ARTMAP model. | ||
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10.1007/s00500-015-1700-y doi (DE-627)SPR006488684 (SPR)s00500-015-1700-y-e DE-627 ger DE-627 rakwb eng Wu, Ke verfasserin aut Adaptive pixel unmixing based on a fuzzy ARTMAP neural network with selective endmembers 2015 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Pixel unmixing is essential for the reliable description of many land-cover patterns with low spatial resolution. The fuzzy ARTMAP neural network-based model has been proven effective for pixel unmixing in the literature. In most cases, the forms of the endmember combination in diverse pixels are very distinct. However, traditional fuzzy ARTMAP model neglects such difference and models endmembers as fixed composition entities. Due to this limitation, the mixture model is unable to precisely and effectively represent details in the result image. In this work, we address this issue by applying a new selective endmember spectral mixture model based on fuzzy ARTMAP neural network. We first consider the endmember variability and identify the most suitable form of the endmember combination, and then the fuzzy ARTMAP model is used to perform the unmixing work. Through two experiments, we show that a more accurate endmember combination in the parent pixel results in an adaptive representation image. The results confirm that the proposed algorithm can effectively improve the accuracy of the unmixing results compared with the linear unmixing method and the traditional fuzzy ARTMAP model. Mixed pixel (dpeaa)DE-He213 Pixel unmixing (dpeaa)DE-He213 Fuzzy ARTMAP (dpeaa)DE-He213 Endmember (dpeaa)DE-He213 Wei, Lifei verfasserin aut Wang, Xianmin verfasserin aut Niu, Ruiqing verfasserin aut Enthalten in Soft Computing Springer-Verlag, 2003 20(2015), 12 vom: 08. Mai, Seite 4723-4732 (DE-627)SPR006469531 nnns volume:20 year:2015 number:12 day:08 month:05 pages:4723-4732 https://dx.doi.org/10.1007/s00500-015-1700-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 20 2015 12 08 05 4723-4732 |
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10.1007/s00500-015-1700-y doi (DE-627)SPR006488684 (SPR)s00500-015-1700-y-e DE-627 ger DE-627 rakwb eng Wu, Ke verfasserin aut Adaptive pixel unmixing based on a fuzzy ARTMAP neural network with selective endmembers 2015 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Pixel unmixing is essential for the reliable description of many land-cover patterns with low spatial resolution. The fuzzy ARTMAP neural network-based model has been proven effective for pixel unmixing in the literature. In most cases, the forms of the endmember combination in diverse pixels are very distinct. However, traditional fuzzy ARTMAP model neglects such difference and models endmembers as fixed composition entities. Due to this limitation, the mixture model is unable to precisely and effectively represent details in the result image. In this work, we address this issue by applying a new selective endmember spectral mixture model based on fuzzy ARTMAP neural network. We first consider the endmember variability and identify the most suitable form of the endmember combination, and then the fuzzy ARTMAP model is used to perform the unmixing work. Through two experiments, we show that a more accurate endmember combination in the parent pixel results in an adaptive representation image. The results confirm that the proposed algorithm can effectively improve the accuracy of the unmixing results compared with the linear unmixing method and the traditional fuzzy ARTMAP model. Mixed pixel (dpeaa)DE-He213 Pixel unmixing (dpeaa)DE-He213 Fuzzy ARTMAP (dpeaa)DE-He213 Endmember (dpeaa)DE-He213 Wei, Lifei verfasserin aut Wang, Xianmin verfasserin aut Niu, Ruiqing verfasserin aut Enthalten in Soft Computing Springer-Verlag, 2003 20(2015), 12 vom: 08. Mai, Seite 4723-4732 (DE-627)SPR006469531 nnns volume:20 year:2015 number:12 day:08 month:05 pages:4723-4732 https://dx.doi.org/10.1007/s00500-015-1700-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 20 2015 12 08 05 4723-4732 |
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10.1007/s00500-015-1700-y doi (DE-627)SPR006488684 (SPR)s00500-015-1700-y-e DE-627 ger DE-627 rakwb eng Wu, Ke verfasserin aut Adaptive pixel unmixing based on a fuzzy ARTMAP neural network with selective endmembers 2015 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Pixel unmixing is essential for the reliable description of many land-cover patterns with low spatial resolution. The fuzzy ARTMAP neural network-based model has been proven effective for pixel unmixing in the literature. In most cases, the forms of the endmember combination in diverse pixels are very distinct. However, traditional fuzzy ARTMAP model neglects such difference and models endmembers as fixed composition entities. Due to this limitation, the mixture model is unable to precisely and effectively represent details in the result image. In this work, we address this issue by applying a new selective endmember spectral mixture model based on fuzzy ARTMAP neural network. We first consider the endmember variability and identify the most suitable form of the endmember combination, and then the fuzzy ARTMAP model is used to perform the unmixing work. Through two experiments, we show that a more accurate endmember combination in the parent pixel results in an adaptive representation image. The results confirm that the proposed algorithm can effectively improve the accuracy of the unmixing results compared with the linear unmixing method and the traditional fuzzy ARTMAP model. Mixed pixel (dpeaa)DE-He213 Pixel unmixing (dpeaa)DE-He213 Fuzzy ARTMAP (dpeaa)DE-He213 Endmember (dpeaa)DE-He213 Wei, Lifei verfasserin aut Wang, Xianmin verfasserin aut Niu, Ruiqing verfasserin aut Enthalten in Soft Computing Springer-Verlag, 2003 20(2015), 12 vom: 08. Mai, Seite 4723-4732 (DE-627)SPR006469531 nnns volume:20 year:2015 number:12 day:08 month:05 pages:4723-4732 https://dx.doi.org/10.1007/s00500-015-1700-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 20 2015 12 08 05 4723-4732 |
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10.1007/s00500-015-1700-y doi (DE-627)SPR006488684 (SPR)s00500-015-1700-y-e DE-627 ger DE-627 rakwb eng Wu, Ke verfasserin aut Adaptive pixel unmixing based on a fuzzy ARTMAP neural network with selective endmembers 2015 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Pixel unmixing is essential for the reliable description of many land-cover patterns with low spatial resolution. The fuzzy ARTMAP neural network-based model has been proven effective for pixel unmixing in the literature. In most cases, the forms of the endmember combination in diverse pixels are very distinct. However, traditional fuzzy ARTMAP model neglects such difference and models endmembers as fixed composition entities. Due to this limitation, the mixture model is unable to precisely and effectively represent details in the result image. In this work, we address this issue by applying a new selective endmember spectral mixture model based on fuzzy ARTMAP neural network. We first consider the endmember variability and identify the most suitable form of the endmember combination, and then the fuzzy ARTMAP model is used to perform the unmixing work. Through two experiments, we show that a more accurate endmember combination in the parent pixel results in an adaptive representation image. The results confirm that the proposed algorithm can effectively improve the accuracy of the unmixing results compared with the linear unmixing method and the traditional fuzzy ARTMAP model. Mixed pixel (dpeaa)DE-He213 Pixel unmixing (dpeaa)DE-He213 Fuzzy ARTMAP (dpeaa)DE-He213 Endmember (dpeaa)DE-He213 Wei, Lifei verfasserin aut Wang, Xianmin verfasserin aut Niu, Ruiqing verfasserin aut Enthalten in Soft Computing Springer-Verlag, 2003 20(2015), 12 vom: 08. Mai, Seite 4723-4732 (DE-627)SPR006469531 nnns volume:20 year:2015 number:12 day:08 month:05 pages:4723-4732 https://dx.doi.org/10.1007/s00500-015-1700-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 20 2015 12 08 05 4723-4732 |
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10.1007/s00500-015-1700-y doi (DE-627)SPR006488684 (SPR)s00500-015-1700-y-e DE-627 ger DE-627 rakwb eng Wu, Ke verfasserin aut Adaptive pixel unmixing based on a fuzzy ARTMAP neural network with selective endmembers 2015 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Pixel unmixing is essential for the reliable description of many land-cover patterns with low spatial resolution. The fuzzy ARTMAP neural network-based model has been proven effective for pixel unmixing in the literature. In most cases, the forms of the endmember combination in diverse pixels are very distinct. However, traditional fuzzy ARTMAP model neglects such difference and models endmembers as fixed composition entities. Due to this limitation, the mixture model is unable to precisely and effectively represent details in the result image. In this work, we address this issue by applying a new selective endmember spectral mixture model based on fuzzy ARTMAP neural network. We first consider the endmember variability and identify the most suitable form of the endmember combination, and then the fuzzy ARTMAP model is used to perform the unmixing work. Through two experiments, we show that a more accurate endmember combination in the parent pixel results in an adaptive representation image. The results confirm that the proposed algorithm can effectively improve the accuracy of the unmixing results compared with the linear unmixing method and the traditional fuzzy ARTMAP model. Mixed pixel (dpeaa)DE-He213 Pixel unmixing (dpeaa)DE-He213 Fuzzy ARTMAP (dpeaa)DE-He213 Endmember (dpeaa)DE-He213 Wei, Lifei verfasserin aut Wang, Xianmin verfasserin aut Niu, Ruiqing verfasserin aut Enthalten in Soft Computing Springer-Verlag, 2003 20(2015), 12 vom: 08. Mai, Seite 4723-4732 (DE-627)SPR006469531 nnns volume:20 year:2015 number:12 day:08 month:05 pages:4723-4732 https://dx.doi.org/10.1007/s00500-015-1700-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 20 2015 12 08 05 4723-4732 |
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Abstract Pixel unmixing is essential for the reliable description of many land-cover patterns with low spatial resolution. The fuzzy ARTMAP neural network-based model has been proven effective for pixel unmixing in the literature. In most cases, the forms of the endmember combination in diverse pixels are very distinct. However, traditional fuzzy ARTMAP model neglects such difference and models endmembers as fixed composition entities. Due to this limitation, the mixture model is unable to precisely and effectively represent details in the result image. In this work, we address this issue by applying a new selective endmember spectral mixture model based on fuzzy ARTMAP neural network. We first consider the endmember variability and identify the most suitable form of the endmember combination, and then the fuzzy ARTMAP model is used to perform the unmixing work. Through two experiments, we show that a more accurate endmember combination in the parent pixel results in an adaptive representation image. The results confirm that the proposed algorithm can effectively improve the accuracy of the unmixing results compared with the linear unmixing method and the traditional fuzzy ARTMAP model. |
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Abstract Pixel unmixing is essential for the reliable description of many land-cover patterns with low spatial resolution. The fuzzy ARTMAP neural network-based model has been proven effective for pixel unmixing in the literature. In most cases, the forms of the endmember combination in diverse pixels are very distinct. However, traditional fuzzy ARTMAP model neglects such difference and models endmembers as fixed composition entities. Due to this limitation, the mixture model is unable to precisely and effectively represent details in the result image. In this work, we address this issue by applying a new selective endmember spectral mixture model based on fuzzy ARTMAP neural network. We first consider the endmember variability and identify the most suitable form of the endmember combination, and then the fuzzy ARTMAP model is used to perform the unmixing work. Through two experiments, we show that a more accurate endmember combination in the parent pixel results in an adaptive representation image. The results confirm that the proposed algorithm can effectively improve the accuracy of the unmixing results compared with the linear unmixing method and the traditional fuzzy ARTMAP model. |
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Abstract Pixel unmixing is essential for the reliable description of many land-cover patterns with low spatial resolution. The fuzzy ARTMAP neural network-based model has been proven effective for pixel unmixing in the literature. In most cases, the forms of the endmember combination in diverse pixels are very distinct. However, traditional fuzzy ARTMAP model neglects such difference and models endmembers as fixed composition entities. Due to this limitation, the mixture model is unable to precisely and effectively represent details in the result image. In this work, we address this issue by applying a new selective endmember spectral mixture model based on fuzzy ARTMAP neural network. We first consider the endmember variability and identify the most suitable form of the endmember combination, and then the fuzzy ARTMAP model is used to perform the unmixing work. Through two experiments, we show that a more accurate endmember combination in the parent pixel results in an adaptive representation image. The results confirm that the proposed algorithm can effectively improve the accuracy of the unmixing results compared with the linear unmixing method and the traditional fuzzy ARTMAP model. |
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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">SPR006488684</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20201124002813.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">201005s2015 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s00500-015-1700-y</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)SPR006488684</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(SPR)s00500-015-1700-y-e</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></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Wu, Ke</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Adaptive pixel unmixing based on a fuzzy ARTMAP neural network with selective endmembers</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2015</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">Abstract Pixel unmixing is essential for the reliable description of many land-cover patterns with low spatial resolution. The fuzzy ARTMAP neural network-based model has been proven effective for pixel unmixing in the literature. In most cases, the forms of the endmember combination in diverse pixels are very distinct. However, traditional fuzzy ARTMAP model neglects such difference and models endmembers as fixed composition entities. Due to this limitation, the mixture model is unable to precisely and effectively represent details in the result image. In this work, we address this issue by applying a new selective endmember spectral mixture model based on fuzzy ARTMAP neural network. We first consider the endmember variability and identify the most suitable form of the endmember combination, and then the fuzzy ARTMAP model is used to perform the unmixing work. Through two experiments, we show that a more accurate endmember combination in the parent pixel results in an adaptive representation image. The results confirm that the proposed algorithm can effectively improve the accuracy of the unmixing results compared with the linear unmixing method and the traditional fuzzy ARTMAP model.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Mixed pixel</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Pixel unmixing</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Fuzzy ARTMAP</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Endmember</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Wei, Lifei</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Wang, Xianmin</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Niu, Ruiqing</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Soft Computing</subfield><subfield code="d">Springer-Verlag, 2003</subfield><subfield code="g">20(2015), 12 vom: 08. Mai, Seite 4723-4732</subfield><subfield code="w">(DE-627)SPR006469531</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:20</subfield><subfield code="g">year:2015</subfield><subfield code="g">number:12</subfield><subfield code="g">day:08</subfield><subfield code="g">month:05</subfield><subfield code="g">pages:4723-4732</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://dx.doi.org/10.1007/s00500-015-1700-y</subfield><subfield code="z">lizenzpflichtig</subfield><subfield code="3">Volltext</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_SPRINGER</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">20</subfield><subfield code="j">2015</subfield><subfield code="e">12</subfield><subfield code="b">08</subfield><subfield code="c">05</subfield><subfield code="h">4723-4732</subfield></datafield></record></collection>
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