Mutual-Information-Based Semi-Supervised Hyperspectral Band Selection With High Discrimination, High Information, and Low Redundancy
The large number of spectral bands in hyperspectral images provides abundant information to distinguish different land covers. However, these spectral bands have much redundancy and bring an extra computational burden. Thus, band selection is important for hyperspectral images. Since the labeled sam...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Jie Feng [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2015 |
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| Übergeordnetes Werk: |
Enthalten in: IEEE transactions on geoscience and remote sensing - New York, NY : IEEE, 1964, 53(2015), 5, Seite 2956-2969 |
|---|---|
| Übergeordnetes Werk: |
volume:53 ; year:2015 ; number:5 ; pages:2956-2969 |
| Links: |
|---|
| DOI / URN: |
10.1109/TGRS.2014.2367022 |
|---|
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OLC1965771629 |
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| 520 | |a The large number of spectral bands in hyperspectral images provides abundant information to distinguish different land covers. However, these spectral bands have much redundancy and bring an extra computational burden. Thus, band selection is important for hyperspectral images. Since the labeled samples are difficult to obtain, a semi-supervised criterion based on maximum discrimination and information (MDI) is defined by using both limited labeled samples and sufficient unlabeled samples. This MDI criterion aims to select the most highly discriminative and informative bands, but it is hard to accurately calculate. Therefore, a novel criterion based on high discrimination, high information, and low redundancy (DIR) is proposed as its low-order approximation. Moreover, from an information theory perspective, a theoretical proof is given that many traditional semi-supervised feature selection criteria are the low-order approximations of this MDI criterion. Compared with them, the proposed criterion needs more relaxed approximation conditions. To search and optimize the proposed criterion, a novel clonal selection algorithm is proposed, where the adaptive clone and mutation operators are devised to speed up the convergence. Experimental results on hyperspectral images demonstrate the effectiveness of the proposed semi-supervised band selection method. | ||
| 650 | 4 | |a learning (artificial intelligence) | |
| 650 | 4 | |a feature selection | |
| 650 | 4 | |a land cover | |
| 650 | 4 | |a Educational institutions | |
| 650 | 4 | |a spectral band | |
| 650 | 4 | |a MDI criterion | |
| 650 | 4 | |a limited labeled sample | |
| 650 | 4 | |a Approximation methods | |
| 650 | 4 | |a Clonal selection algorithm (CSA) | |
| 650 | 4 | |a convergence | |
| 650 | 4 | |a Entropy | |
| 650 | 4 | |a multivariable mutual information (MMI) | |
| 650 | 4 | |a maximum discrimination and information | |
| 650 | 4 | |a hyperspectral images | |
| 650 | 4 | |a approximation theory | |
| 650 | 4 | |a unlabeled samples | |
| 650 | 4 | |a hyperspectral imaging | |
| 650 | 4 | |a DIR | |
| 650 | 4 | |a clonal selection algorithm | |
| 650 | 4 | |a low-order approximation | |
| 650 | 4 | |a semisupervised learning | |
| 650 | 4 | |a mutual information-based semisupervised hyperspectral band selection | |
| 650 | 4 | |a information theory | |
| 650 | 4 | |a redundancy | |
| 650 | 4 | |a hyperspectral band selection | |
| 650 | 4 | |a mutation operators | |
| 650 | 4 | |a adaptive clone operator | |
| 650 | 4 | |a discrimination information redundancy | |
| 650 | 4 | |a geophysical image processing | |
| 650 | 4 | |a semi-supervised feature selection criteria | |
| 650 | 4 | |a Information theory | |
| 700 | 0 | |a Licheng Jiao |4 oth | |
| 700 | 0 | |a Fang Liu |4 oth | |
| 700 | 0 | |a Tao Sun |4 oth | |
| 700 | 0 | |a Xiangrong Zhang |4 oth | |
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| 773 | 1 | 8 | |g volume:53 |g year:2015 |g number:5 |g pages:2956-2969 |
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10.1109/TGRS.2014.2367022 doi PQ20160617 (DE-627)OLC1965771629 (DE-599)GBVOLC1965771629 (PRQ)c2413-b18e9cddf41ed9c7391bfda189617ed74592a8d24a215acf5b4e3f2be188cc170 (KEY)0048677920150000053000502956mutualinformationbasedsemisupervisedhyperspectralb DE-627 ger DE-627 rakwb eng 620 550 DNB Jie Feng verfasserin aut Mutual-Information-Based Semi-Supervised Hyperspectral Band Selection With High Discrimination, High Information, and Low Redundancy 2015 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier The large number of spectral bands in hyperspectral images provides abundant information to distinguish different land covers. However, these spectral bands have much redundancy and bring an extra computational burden. Thus, band selection is important for hyperspectral images. Since the labeled samples are difficult to obtain, a semi-supervised criterion based on maximum discrimination and information (MDI) is defined by using both limited labeled samples and sufficient unlabeled samples. This MDI criterion aims to select the most highly discriminative and informative bands, but it is hard to accurately calculate. Therefore, a novel criterion based on high discrimination, high information, and low redundancy (DIR) is proposed as its low-order approximation. Moreover, from an information theory perspective, a theoretical proof is given that many traditional semi-supervised feature selection criteria are the low-order approximations of this MDI criterion. Compared with them, the proposed criterion needs more relaxed approximation conditions. To search and optimize the proposed criterion, a novel clonal selection algorithm is proposed, where the adaptive clone and mutation operators are devised to speed up the convergence. Experimental results on hyperspectral images demonstrate the effectiveness of the proposed semi-supervised band selection method. learning (artificial intelligence) feature selection land cover Educational institutions spectral band MDI criterion limited labeled sample Approximation methods Clonal selection algorithm (CSA) convergence Entropy multivariable mutual information (MMI) maximum discrimination and information hyperspectral images approximation theory unlabeled samples hyperspectral imaging DIR clonal selection algorithm low-order approximation semisupervised learning mutual information-based semisupervised hyperspectral band selection information theory redundancy hyperspectral band selection mutation operators adaptive clone operator discrimination information redundancy geophysical image processing semi-supervised feature selection criteria Information theory Licheng Jiao oth Fang Liu oth Tao Sun oth Xiangrong Zhang oth Enthalten in IEEE transactions on geoscience and remote sensing New York, NY : IEEE, 1964 53(2015), 5, Seite 2956-2969 (DE-627)129601667 (DE-600)241439-9 (DE-576)015095282 0196-2892 nnns volume:53 year:2015 number:5 pages:2956-2969 http://dx.doi.org/10.1109/TGRS.2014.2367022 Volltext http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6977960 http://search.proquest.com/docview/1644050765 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-ARC SSG-OLC-TEC SSG-OLC-GEO SSG-OLC-FOR SSG-OPC-GGO SSG-OPC-GEO GBV_ILN_70 GBV_ILN_2027 AR 53 2015 5 2956-2969 |
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10.1109/TGRS.2014.2367022 doi PQ20160617 (DE-627)OLC1965771629 (DE-599)GBVOLC1965771629 (PRQ)c2413-b18e9cddf41ed9c7391bfda189617ed74592a8d24a215acf5b4e3f2be188cc170 (KEY)0048677920150000053000502956mutualinformationbasedsemisupervisedhyperspectralb DE-627 ger DE-627 rakwb eng 620 550 DNB Jie Feng verfasserin aut Mutual-Information-Based Semi-Supervised Hyperspectral Band Selection With High Discrimination, High Information, and Low Redundancy 2015 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier The large number of spectral bands in hyperspectral images provides abundant information to distinguish different land covers. However, these spectral bands have much redundancy and bring an extra computational burden. Thus, band selection is important for hyperspectral images. Since the labeled samples are difficult to obtain, a semi-supervised criterion based on maximum discrimination and information (MDI) is defined by using both limited labeled samples and sufficient unlabeled samples. This MDI criterion aims to select the most highly discriminative and informative bands, but it is hard to accurately calculate. Therefore, a novel criterion based on high discrimination, high information, and low redundancy (DIR) is proposed as its low-order approximation. Moreover, from an information theory perspective, a theoretical proof is given that many traditional semi-supervised feature selection criteria are the low-order approximations of this MDI criterion. Compared with them, the proposed criterion needs more relaxed approximation conditions. To search and optimize the proposed criterion, a novel clonal selection algorithm is proposed, where the adaptive clone and mutation operators are devised to speed up the convergence. Experimental results on hyperspectral images demonstrate the effectiveness of the proposed semi-supervised band selection method. learning (artificial intelligence) feature selection land cover Educational institutions spectral band MDI criterion limited labeled sample Approximation methods Clonal selection algorithm (CSA) convergence Entropy multivariable mutual information (MMI) maximum discrimination and information hyperspectral images approximation theory unlabeled samples hyperspectral imaging DIR clonal selection algorithm low-order approximation semisupervised learning mutual information-based semisupervised hyperspectral band selection information theory redundancy hyperspectral band selection mutation operators adaptive clone operator discrimination information redundancy geophysical image processing semi-supervised feature selection criteria Information theory Licheng Jiao oth Fang Liu oth Tao Sun oth Xiangrong Zhang oth Enthalten in IEEE transactions on geoscience and remote sensing New York, NY : IEEE, 1964 53(2015), 5, Seite 2956-2969 (DE-627)129601667 (DE-600)241439-9 (DE-576)015095282 0196-2892 nnns volume:53 year:2015 number:5 pages:2956-2969 http://dx.doi.org/10.1109/TGRS.2014.2367022 Volltext http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6977960 http://search.proquest.com/docview/1644050765 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-ARC SSG-OLC-TEC SSG-OLC-GEO SSG-OLC-FOR SSG-OPC-GGO SSG-OPC-GEO GBV_ILN_70 GBV_ILN_2027 AR 53 2015 5 2956-2969 |
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10.1109/TGRS.2014.2367022 doi PQ20160617 (DE-627)OLC1965771629 (DE-599)GBVOLC1965771629 (PRQ)c2413-b18e9cddf41ed9c7391bfda189617ed74592a8d24a215acf5b4e3f2be188cc170 (KEY)0048677920150000053000502956mutualinformationbasedsemisupervisedhyperspectralb DE-627 ger DE-627 rakwb eng 620 550 DNB Jie Feng verfasserin aut Mutual-Information-Based Semi-Supervised Hyperspectral Band Selection With High Discrimination, High Information, and Low Redundancy 2015 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier The large number of spectral bands in hyperspectral images provides abundant information to distinguish different land covers. However, these spectral bands have much redundancy and bring an extra computational burden. Thus, band selection is important for hyperspectral images. Since the labeled samples are difficult to obtain, a semi-supervised criterion based on maximum discrimination and information (MDI) is defined by using both limited labeled samples and sufficient unlabeled samples. This MDI criterion aims to select the most highly discriminative and informative bands, but it is hard to accurately calculate. Therefore, a novel criterion based on high discrimination, high information, and low redundancy (DIR) is proposed as its low-order approximation. Moreover, from an information theory perspective, a theoretical proof is given that many traditional semi-supervised feature selection criteria are the low-order approximations of this MDI criterion. Compared with them, the proposed criterion needs more relaxed approximation conditions. To search and optimize the proposed criterion, a novel clonal selection algorithm is proposed, where the adaptive clone and mutation operators are devised to speed up the convergence. Experimental results on hyperspectral images demonstrate the effectiveness of the proposed semi-supervised band selection method. learning (artificial intelligence) feature selection land cover Educational institutions spectral band MDI criterion limited labeled sample Approximation methods Clonal selection algorithm (CSA) convergence Entropy multivariable mutual information (MMI) maximum discrimination and information hyperspectral images approximation theory unlabeled samples hyperspectral imaging DIR clonal selection algorithm low-order approximation semisupervised learning mutual information-based semisupervised hyperspectral band selection information theory redundancy hyperspectral band selection mutation operators adaptive clone operator discrimination information redundancy geophysical image processing semi-supervised feature selection criteria Information theory Licheng Jiao oth Fang Liu oth Tao Sun oth Xiangrong Zhang oth Enthalten in IEEE transactions on geoscience and remote sensing New York, NY : IEEE, 1964 53(2015), 5, Seite 2956-2969 (DE-627)129601667 (DE-600)241439-9 (DE-576)015095282 0196-2892 nnns volume:53 year:2015 number:5 pages:2956-2969 http://dx.doi.org/10.1109/TGRS.2014.2367022 Volltext http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6977960 http://search.proquest.com/docview/1644050765 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-ARC SSG-OLC-TEC SSG-OLC-GEO SSG-OLC-FOR SSG-OPC-GGO SSG-OPC-GEO GBV_ILN_70 GBV_ILN_2027 AR 53 2015 5 2956-2969 |
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10.1109/TGRS.2014.2367022 doi PQ20160617 (DE-627)OLC1965771629 (DE-599)GBVOLC1965771629 (PRQ)c2413-b18e9cddf41ed9c7391bfda189617ed74592a8d24a215acf5b4e3f2be188cc170 (KEY)0048677920150000053000502956mutualinformationbasedsemisupervisedhyperspectralb DE-627 ger DE-627 rakwb eng 620 550 DNB Jie Feng verfasserin aut Mutual-Information-Based Semi-Supervised Hyperspectral Band Selection With High Discrimination, High Information, and Low Redundancy 2015 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier The large number of spectral bands in hyperspectral images provides abundant information to distinguish different land covers. However, these spectral bands have much redundancy and bring an extra computational burden. Thus, band selection is important for hyperspectral images. Since the labeled samples are difficult to obtain, a semi-supervised criterion based on maximum discrimination and information (MDI) is defined by using both limited labeled samples and sufficient unlabeled samples. This MDI criterion aims to select the most highly discriminative and informative bands, but it is hard to accurately calculate. Therefore, a novel criterion based on high discrimination, high information, and low redundancy (DIR) is proposed as its low-order approximation. Moreover, from an information theory perspective, a theoretical proof is given that many traditional semi-supervised feature selection criteria are the low-order approximations of this MDI criterion. Compared with them, the proposed criterion needs more relaxed approximation conditions. To search and optimize the proposed criterion, a novel clonal selection algorithm is proposed, where the adaptive clone and mutation operators are devised to speed up the convergence. Experimental results on hyperspectral images demonstrate the effectiveness of the proposed semi-supervised band selection method. learning (artificial intelligence) feature selection land cover Educational institutions spectral band MDI criterion limited labeled sample Approximation methods Clonal selection algorithm (CSA) convergence Entropy multivariable mutual information (MMI) maximum discrimination and information hyperspectral images approximation theory unlabeled samples hyperspectral imaging DIR clonal selection algorithm low-order approximation semisupervised learning mutual information-based semisupervised hyperspectral band selection information theory redundancy hyperspectral band selection mutation operators adaptive clone operator discrimination information redundancy geophysical image processing semi-supervised feature selection criteria Information theory Licheng Jiao oth Fang Liu oth Tao Sun oth Xiangrong Zhang oth Enthalten in IEEE transactions on geoscience and remote sensing New York, NY : IEEE, 1964 53(2015), 5, Seite 2956-2969 (DE-627)129601667 (DE-600)241439-9 (DE-576)015095282 0196-2892 nnns volume:53 year:2015 number:5 pages:2956-2969 http://dx.doi.org/10.1109/TGRS.2014.2367022 Volltext http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6977960 http://search.proquest.com/docview/1644050765 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-ARC SSG-OLC-TEC SSG-OLC-GEO SSG-OLC-FOR SSG-OPC-GGO SSG-OPC-GEO GBV_ILN_70 GBV_ILN_2027 AR 53 2015 5 2956-2969 |
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10.1109/TGRS.2014.2367022 doi PQ20160617 (DE-627)OLC1965771629 (DE-599)GBVOLC1965771629 (PRQ)c2413-b18e9cddf41ed9c7391bfda189617ed74592a8d24a215acf5b4e3f2be188cc170 (KEY)0048677920150000053000502956mutualinformationbasedsemisupervisedhyperspectralb DE-627 ger DE-627 rakwb eng 620 550 DNB Jie Feng verfasserin aut Mutual-Information-Based Semi-Supervised Hyperspectral Band Selection With High Discrimination, High Information, and Low Redundancy 2015 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier The large number of spectral bands in hyperspectral images provides abundant information to distinguish different land covers. However, these spectral bands have much redundancy and bring an extra computational burden. Thus, band selection is important for hyperspectral images. Since the labeled samples are difficult to obtain, a semi-supervised criterion based on maximum discrimination and information (MDI) is defined by using both limited labeled samples and sufficient unlabeled samples. This MDI criterion aims to select the most highly discriminative and informative bands, but it is hard to accurately calculate. Therefore, a novel criterion based on high discrimination, high information, and low redundancy (DIR) is proposed as its low-order approximation. Moreover, from an information theory perspective, a theoretical proof is given that many traditional semi-supervised feature selection criteria are the low-order approximations of this MDI criterion. Compared with them, the proposed criterion needs more relaxed approximation conditions. To search and optimize the proposed criterion, a novel clonal selection algorithm is proposed, where the adaptive clone and mutation operators are devised to speed up the convergence. Experimental results on hyperspectral images demonstrate the effectiveness of the proposed semi-supervised band selection method. learning (artificial intelligence) feature selection land cover Educational institutions spectral band MDI criterion limited labeled sample Approximation methods Clonal selection algorithm (CSA) convergence Entropy multivariable mutual information (MMI) maximum discrimination and information hyperspectral images approximation theory unlabeled samples hyperspectral imaging DIR clonal selection algorithm low-order approximation semisupervised learning mutual information-based semisupervised hyperspectral band selection information theory redundancy hyperspectral band selection mutation operators adaptive clone operator discrimination information redundancy geophysical image processing semi-supervised feature selection criteria Information theory Licheng Jiao oth Fang Liu oth Tao Sun oth Xiangrong Zhang oth Enthalten in IEEE transactions on geoscience and remote sensing New York, NY : IEEE, 1964 53(2015), 5, Seite 2956-2969 (DE-627)129601667 (DE-600)241439-9 (DE-576)015095282 0196-2892 nnns volume:53 year:2015 number:5 pages:2956-2969 http://dx.doi.org/10.1109/TGRS.2014.2367022 Volltext http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6977960 http://search.proquest.com/docview/1644050765 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-ARC SSG-OLC-TEC SSG-OLC-GEO SSG-OLC-FOR SSG-OPC-GGO SSG-OPC-GEO GBV_ILN_70 GBV_ILN_2027 AR 53 2015 5 2956-2969 |
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Jie Feng ddc 620 misc learning (artificial intelligence) misc feature selection misc land cover misc Educational institutions misc spectral band misc MDI criterion misc limited labeled sample misc Approximation methods misc Clonal selection algorithm (CSA) misc convergence misc Entropy misc multivariable mutual information (MMI) misc maximum discrimination and information misc hyperspectral images misc approximation theory misc unlabeled samples misc hyperspectral imaging misc DIR misc clonal selection algorithm misc low-order approximation misc semisupervised learning misc mutual information-based semisupervised hyperspectral band selection misc information theory misc redundancy misc hyperspectral band selection misc mutation operators misc adaptive clone operator misc discrimination information redundancy misc geophysical image processing misc semi-supervised feature selection criteria misc Information theory Mutual-Information-Based Semi-Supervised Hyperspectral Band Selection With High Discrimination, High Information, and Low Redundancy |
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620 550 DNB Mutual-Information-Based Semi-Supervised Hyperspectral Band Selection With High Discrimination, High Information, and Low Redundancy learning (artificial intelligence) feature selection land cover Educational institutions spectral band MDI criterion limited labeled sample Approximation methods Clonal selection algorithm (CSA) convergence Entropy multivariable mutual information (MMI) maximum discrimination and information hyperspectral images approximation theory unlabeled samples hyperspectral imaging DIR clonal selection algorithm low-order approximation semisupervised learning mutual information-based semisupervised hyperspectral band selection information theory redundancy hyperspectral band selection mutation operators adaptive clone operator discrimination information redundancy geophysical image processing semi-supervised feature selection criteria Information theory |
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Mutual-Information-Based Semi-Supervised Hyperspectral Band Selection With High Discrimination, High Information, and Low Redundancy |
| abstract |
The large number of spectral bands in hyperspectral images provides abundant information to distinguish different land covers. However, these spectral bands have much redundancy and bring an extra computational burden. Thus, band selection is important for hyperspectral images. Since the labeled samples are difficult to obtain, a semi-supervised criterion based on maximum discrimination and information (MDI) is defined by using both limited labeled samples and sufficient unlabeled samples. This MDI criterion aims to select the most highly discriminative and informative bands, but it is hard to accurately calculate. Therefore, a novel criterion based on high discrimination, high information, and low redundancy (DIR) is proposed as its low-order approximation. Moreover, from an information theory perspective, a theoretical proof is given that many traditional semi-supervised feature selection criteria are the low-order approximations of this MDI criterion. Compared with them, the proposed criterion needs more relaxed approximation conditions. To search and optimize the proposed criterion, a novel clonal selection algorithm is proposed, where the adaptive clone and mutation operators are devised to speed up the convergence. Experimental results on hyperspectral images demonstrate the effectiveness of the proposed semi-supervised band selection method. |
| abstractGer |
The large number of spectral bands in hyperspectral images provides abundant information to distinguish different land covers. However, these spectral bands have much redundancy and bring an extra computational burden. Thus, band selection is important for hyperspectral images. Since the labeled samples are difficult to obtain, a semi-supervised criterion based on maximum discrimination and information (MDI) is defined by using both limited labeled samples and sufficient unlabeled samples. This MDI criterion aims to select the most highly discriminative and informative bands, but it is hard to accurately calculate. Therefore, a novel criterion based on high discrimination, high information, and low redundancy (DIR) is proposed as its low-order approximation. Moreover, from an information theory perspective, a theoretical proof is given that many traditional semi-supervised feature selection criteria are the low-order approximations of this MDI criterion. Compared with them, the proposed criterion needs more relaxed approximation conditions. To search and optimize the proposed criterion, a novel clonal selection algorithm is proposed, where the adaptive clone and mutation operators are devised to speed up the convergence. Experimental results on hyperspectral images demonstrate the effectiveness of the proposed semi-supervised band selection method. |
| abstract_unstemmed |
The large number of spectral bands in hyperspectral images provides abundant information to distinguish different land covers. However, these spectral bands have much redundancy and bring an extra computational burden. Thus, band selection is important for hyperspectral images. Since the labeled samples are difficult to obtain, a semi-supervised criterion based on maximum discrimination and information (MDI) is defined by using both limited labeled samples and sufficient unlabeled samples. This MDI criterion aims to select the most highly discriminative and informative bands, but it is hard to accurately calculate. Therefore, a novel criterion based on high discrimination, high information, and low redundancy (DIR) is proposed as its low-order approximation. Moreover, from an information theory perspective, a theoretical proof is given that many traditional semi-supervised feature selection criteria are the low-order approximations of this MDI criterion. Compared with them, the proposed criterion needs more relaxed approximation conditions. To search and optimize the proposed criterion, a novel clonal selection algorithm is proposed, where the adaptive clone and mutation operators are devised to speed up the convergence. Experimental results on hyperspectral images demonstrate the effectiveness of the proposed semi-supervised band selection method. |
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Mutual-Information-Based Semi-Supervised Hyperspectral Band Selection With High Discrimination, High Information, and Low Redundancy |
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