Multiple features learning for ship classification in optical imagery
Abstract The sea surface vessel/ship classification is a challenging problem with enormous implications to the world’s global supply chain and militaries. The problem is similar to other well-studied problems in object recognition such as face recognition. However, it is more complex since ships’ ap...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Huang, Longhui [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2017 |
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| Schlagwörter: |
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| Anmerkung: |
© Springer Science+Business Media, LLC 2017 |
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| Übergeordnetes Werk: |
Enthalten in: Multimedia tools and applications - Springer US, 1995, 77(2017), 11 vom: 01. Juli, Seite 13363-13389 |
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| Übergeordnetes Werk: |
volume:77 ; year:2017 ; number:11 ; day:01 ; month:07 ; pages:13363-13389 |
| Links: |
|---|
| DOI / URN: |
10.1007/s11042-017-4952-y |
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OLC2035048133 |
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| 520 | |a Abstract The sea surface vessel/ship classification is a challenging problem with enormous implications to the world’s global supply chain and militaries. The problem is similar to other well-studied problems in object recognition such as face recognition. However, it is more complex since ships’ appearance is easily affected by external factors such as lighting or weather conditions, viewing geometry and sea state. The large within-class variations in some vessels also make ship classification more complicated and challenging. In this paper, we propose an effective multiple features learning (MFL) framework for ship classification, which contains three types of features: Gabor-based multi-scale completed local binary patterns (MS-CLBP), patch-based MS-CLBP and Fisher vector, and combination of Bag of visual words (BOVW) and spatial pyramid matching (SPM). After multiple feature learning, feature-level fusion and decision-level fusion are both investigated for final classification. In the proposed framework, typical support vector machine (SVM) classifier is employed to provide posterior-probability estimation. Experimental results on remote sensing ship image datasets demonstrate that the proposed approach shows a consistent improvement on performance when compared to some state-of-the-art methods. | ||
| 650 | 4 | |a Ship classification | |
| 650 | 4 | |a Multiple features learning | |
| 650 | 4 | |a Optical imagery | |
| 650 | 4 | |a Feature-level fusion | |
| 650 | 4 | |a Decision-level fusion | |
| 700 | 1 | |a Li, Wei |0 (orcid)0000-0001-7015-7335 |4 aut | |
| 700 | 1 | |a Chen, Chen |4 aut | |
| 700 | 1 | |a Zhang, Fan |4 aut | |
| 700 | 1 | |a Lang, Haitao |4 aut | |
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10.1007/s11042-017-4952-y doi (DE-627)OLC2035048133 (DE-He213)s11042-017-4952-y-p DE-627 ger DE-627 rakwb eng 070 004 VZ Huang, Longhui verfasserin aut Multiple features learning for ship classification in optical imagery 2017 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media, LLC 2017 Abstract The sea surface vessel/ship classification is a challenging problem with enormous implications to the world’s global supply chain and militaries. The problem is similar to other well-studied problems in object recognition such as face recognition. However, it is more complex since ships’ appearance is easily affected by external factors such as lighting or weather conditions, viewing geometry and sea state. The large within-class variations in some vessels also make ship classification more complicated and challenging. In this paper, we propose an effective multiple features learning (MFL) framework for ship classification, which contains three types of features: Gabor-based multi-scale completed local binary patterns (MS-CLBP), patch-based MS-CLBP and Fisher vector, and combination of Bag of visual words (BOVW) and spatial pyramid matching (SPM). After multiple feature learning, feature-level fusion and decision-level fusion are both investigated for final classification. In the proposed framework, typical support vector machine (SVM) classifier is employed to provide posterior-probability estimation. Experimental results on remote sensing ship image datasets demonstrate that the proposed approach shows a consistent improvement on performance when compared to some state-of-the-art methods. Ship classification Multiple features learning Optical imagery Feature-level fusion Decision-level fusion Li, Wei (orcid)0000-0001-7015-7335 aut Chen, Chen aut Zhang, Fan aut Lang, Haitao aut Enthalten in Multimedia tools and applications Springer US, 1995 77(2017), 11 vom: 01. Juli, Seite 13363-13389 (DE-627)189064145 (DE-600)1287642-2 (DE-576)052842126 1380-7501 nnns volume:77 year:2017 number:11 day:01 month:07 pages:13363-13389 https://doi.org/10.1007/s11042-017-4952-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-BUB SSG-OLC-MKW GBV_ILN_70 AR 77 2017 11 01 07 13363-13389 |
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10.1007/s11042-017-4952-y doi (DE-627)OLC2035048133 (DE-He213)s11042-017-4952-y-p DE-627 ger DE-627 rakwb eng 070 004 VZ Huang, Longhui verfasserin aut Multiple features learning for ship classification in optical imagery 2017 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media, LLC 2017 Abstract The sea surface vessel/ship classification is a challenging problem with enormous implications to the world’s global supply chain and militaries. The problem is similar to other well-studied problems in object recognition such as face recognition. However, it is more complex since ships’ appearance is easily affected by external factors such as lighting or weather conditions, viewing geometry and sea state. The large within-class variations in some vessels also make ship classification more complicated and challenging. In this paper, we propose an effective multiple features learning (MFL) framework for ship classification, which contains three types of features: Gabor-based multi-scale completed local binary patterns (MS-CLBP), patch-based MS-CLBP and Fisher vector, and combination of Bag of visual words (BOVW) and spatial pyramid matching (SPM). After multiple feature learning, feature-level fusion and decision-level fusion are both investigated for final classification. In the proposed framework, typical support vector machine (SVM) classifier is employed to provide posterior-probability estimation. Experimental results on remote sensing ship image datasets demonstrate that the proposed approach shows a consistent improvement on performance when compared to some state-of-the-art methods. Ship classification Multiple features learning Optical imagery Feature-level fusion Decision-level fusion Li, Wei (orcid)0000-0001-7015-7335 aut Chen, Chen aut Zhang, Fan aut Lang, Haitao aut Enthalten in Multimedia tools and applications Springer US, 1995 77(2017), 11 vom: 01. Juli, Seite 13363-13389 (DE-627)189064145 (DE-600)1287642-2 (DE-576)052842126 1380-7501 nnns volume:77 year:2017 number:11 day:01 month:07 pages:13363-13389 https://doi.org/10.1007/s11042-017-4952-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-BUB SSG-OLC-MKW GBV_ILN_70 AR 77 2017 11 01 07 13363-13389 |
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10.1007/s11042-017-4952-y doi (DE-627)OLC2035048133 (DE-He213)s11042-017-4952-y-p DE-627 ger DE-627 rakwb eng 070 004 VZ Huang, Longhui verfasserin aut Multiple features learning for ship classification in optical imagery 2017 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media, LLC 2017 Abstract The sea surface vessel/ship classification is a challenging problem with enormous implications to the world’s global supply chain and militaries. The problem is similar to other well-studied problems in object recognition such as face recognition. However, it is more complex since ships’ appearance is easily affected by external factors such as lighting or weather conditions, viewing geometry and sea state. The large within-class variations in some vessels also make ship classification more complicated and challenging. In this paper, we propose an effective multiple features learning (MFL) framework for ship classification, which contains three types of features: Gabor-based multi-scale completed local binary patterns (MS-CLBP), patch-based MS-CLBP and Fisher vector, and combination of Bag of visual words (BOVW) and spatial pyramid matching (SPM). After multiple feature learning, feature-level fusion and decision-level fusion are both investigated for final classification. In the proposed framework, typical support vector machine (SVM) classifier is employed to provide posterior-probability estimation. Experimental results on remote sensing ship image datasets demonstrate that the proposed approach shows a consistent improvement on performance when compared to some state-of-the-art methods. Ship classification Multiple features learning Optical imagery Feature-level fusion Decision-level fusion Li, Wei (orcid)0000-0001-7015-7335 aut Chen, Chen aut Zhang, Fan aut Lang, Haitao aut Enthalten in Multimedia tools and applications Springer US, 1995 77(2017), 11 vom: 01. Juli, Seite 13363-13389 (DE-627)189064145 (DE-600)1287642-2 (DE-576)052842126 1380-7501 nnns volume:77 year:2017 number:11 day:01 month:07 pages:13363-13389 https://doi.org/10.1007/s11042-017-4952-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-BUB SSG-OLC-MKW GBV_ILN_70 AR 77 2017 11 01 07 13363-13389 |
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10.1007/s11042-017-4952-y doi (DE-627)OLC2035048133 (DE-He213)s11042-017-4952-y-p DE-627 ger DE-627 rakwb eng 070 004 VZ Huang, Longhui verfasserin aut Multiple features learning for ship classification in optical imagery 2017 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media, LLC 2017 Abstract The sea surface vessel/ship classification is a challenging problem with enormous implications to the world’s global supply chain and militaries. The problem is similar to other well-studied problems in object recognition such as face recognition. However, it is more complex since ships’ appearance is easily affected by external factors such as lighting or weather conditions, viewing geometry and sea state. The large within-class variations in some vessels also make ship classification more complicated and challenging. In this paper, we propose an effective multiple features learning (MFL) framework for ship classification, which contains three types of features: Gabor-based multi-scale completed local binary patterns (MS-CLBP), patch-based MS-CLBP and Fisher vector, and combination of Bag of visual words (BOVW) and spatial pyramid matching (SPM). After multiple feature learning, feature-level fusion and decision-level fusion are both investigated for final classification. In the proposed framework, typical support vector machine (SVM) classifier is employed to provide posterior-probability estimation. Experimental results on remote sensing ship image datasets demonstrate that the proposed approach shows a consistent improvement on performance when compared to some state-of-the-art methods. Ship classification Multiple features learning Optical imagery Feature-level fusion Decision-level fusion Li, Wei (orcid)0000-0001-7015-7335 aut Chen, Chen aut Zhang, Fan aut Lang, Haitao aut Enthalten in Multimedia tools and applications Springer US, 1995 77(2017), 11 vom: 01. Juli, Seite 13363-13389 (DE-627)189064145 (DE-600)1287642-2 (DE-576)052842126 1380-7501 nnns volume:77 year:2017 number:11 day:01 month:07 pages:13363-13389 https://doi.org/10.1007/s11042-017-4952-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-BUB SSG-OLC-MKW GBV_ILN_70 AR 77 2017 11 01 07 13363-13389 |
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10.1007/s11042-017-4952-y doi (DE-627)OLC2035048133 (DE-He213)s11042-017-4952-y-p DE-627 ger DE-627 rakwb eng 070 004 VZ Huang, Longhui verfasserin aut Multiple features learning for ship classification in optical imagery 2017 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media, LLC 2017 Abstract The sea surface vessel/ship classification is a challenging problem with enormous implications to the world’s global supply chain and militaries. The problem is similar to other well-studied problems in object recognition such as face recognition. However, it is more complex since ships’ appearance is easily affected by external factors such as lighting or weather conditions, viewing geometry and sea state. The large within-class variations in some vessels also make ship classification more complicated and challenging. In this paper, we propose an effective multiple features learning (MFL) framework for ship classification, which contains three types of features: Gabor-based multi-scale completed local binary patterns (MS-CLBP), patch-based MS-CLBP and Fisher vector, and combination of Bag of visual words (BOVW) and spatial pyramid matching (SPM). After multiple feature learning, feature-level fusion and decision-level fusion are both investigated for final classification. In the proposed framework, typical support vector machine (SVM) classifier is employed to provide posterior-probability estimation. Experimental results on remote sensing ship image datasets demonstrate that the proposed approach shows a consistent improvement on performance when compared to some state-of-the-art methods. Ship classification Multiple features learning Optical imagery Feature-level fusion Decision-level fusion Li, Wei (orcid)0000-0001-7015-7335 aut Chen, Chen aut Zhang, Fan aut Lang, Haitao aut Enthalten in Multimedia tools and applications Springer US, 1995 77(2017), 11 vom: 01. Juli, Seite 13363-13389 (DE-627)189064145 (DE-600)1287642-2 (DE-576)052842126 1380-7501 nnns volume:77 year:2017 number:11 day:01 month:07 pages:13363-13389 https://doi.org/10.1007/s11042-017-4952-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-BUB SSG-OLC-MKW GBV_ILN_70 AR 77 2017 11 01 07 13363-13389 |
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Multiple features learning for ship classification in optical imagery |
| abstract |
Abstract The sea surface vessel/ship classification is a challenging problem with enormous implications to the world’s global supply chain and militaries. The problem is similar to other well-studied problems in object recognition such as face recognition. However, it is more complex since ships’ appearance is easily affected by external factors such as lighting or weather conditions, viewing geometry and sea state. The large within-class variations in some vessels also make ship classification more complicated and challenging. In this paper, we propose an effective multiple features learning (MFL) framework for ship classification, which contains three types of features: Gabor-based multi-scale completed local binary patterns (MS-CLBP), patch-based MS-CLBP and Fisher vector, and combination of Bag of visual words (BOVW) and spatial pyramid matching (SPM). After multiple feature learning, feature-level fusion and decision-level fusion are both investigated for final classification. In the proposed framework, typical support vector machine (SVM) classifier is employed to provide posterior-probability estimation. Experimental results on remote sensing ship image datasets demonstrate that the proposed approach shows a consistent improvement on performance when compared to some state-of-the-art methods. © Springer Science+Business Media, LLC 2017 |
| abstractGer |
Abstract The sea surface vessel/ship classification is a challenging problem with enormous implications to the world’s global supply chain and militaries. The problem is similar to other well-studied problems in object recognition such as face recognition. However, it is more complex since ships’ appearance is easily affected by external factors such as lighting or weather conditions, viewing geometry and sea state. The large within-class variations in some vessels also make ship classification more complicated and challenging. In this paper, we propose an effective multiple features learning (MFL) framework for ship classification, which contains three types of features: Gabor-based multi-scale completed local binary patterns (MS-CLBP), patch-based MS-CLBP and Fisher vector, and combination of Bag of visual words (BOVW) and spatial pyramid matching (SPM). After multiple feature learning, feature-level fusion and decision-level fusion are both investigated for final classification. In the proposed framework, typical support vector machine (SVM) classifier is employed to provide posterior-probability estimation. Experimental results on remote sensing ship image datasets demonstrate that the proposed approach shows a consistent improvement on performance when compared to some state-of-the-art methods. © Springer Science+Business Media, LLC 2017 |
| abstract_unstemmed |
Abstract The sea surface vessel/ship classification is a challenging problem with enormous implications to the world’s global supply chain and militaries. The problem is similar to other well-studied problems in object recognition such as face recognition. However, it is more complex since ships’ appearance is easily affected by external factors such as lighting or weather conditions, viewing geometry and sea state. The large within-class variations in some vessels also make ship classification more complicated and challenging. In this paper, we propose an effective multiple features learning (MFL) framework for ship classification, which contains three types of features: Gabor-based multi-scale completed local binary patterns (MS-CLBP), patch-based MS-CLBP and Fisher vector, and combination of Bag of visual words (BOVW) and spatial pyramid matching (SPM). After multiple feature learning, feature-level fusion and decision-level fusion are both investigated for final classification. In the proposed framework, typical support vector machine (SVM) classifier is employed to provide posterior-probability estimation. Experimental results on remote sensing ship image datasets demonstrate that the proposed approach shows a consistent improvement on performance when compared to some state-of-the-art methods. © Springer Science+Business Media, LLC 2017 |
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GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-BUB SSG-OLC-MKW GBV_ILN_70 |
| container_issue |
11 |
| title_short |
Multiple features learning for ship classification in optical imagery |
| url |
https://doi.org/10.1007/s11042-017-4952-y |
| remote_bool |
false |
| author2 |
Li, Wei Chen, Chen Zhang, Fan Lang, Haitao |
| author2Str |
Li, Wei Chen, Chen Zhang, Fan Lang, Haitao |
| ppnlink |
189064145 |
| mediatype_str_mv |
n |
| isOA_txt |
false |
| hochschulschrift_bool |
false |
| doi_str |
10.1007/s11042-017-4952-y |
| up_date |
2024-07-03T23:35:36.793Z |
| _version_ |
1803602872809553920 |
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7.4008236 |