Extracting Target Spectrum for Hyperspectral Target Detection: An Adaptive Weighted Learning Method Using a Self-Completed Background Dictionary
The accuracy of target spectra determines the performances of hyperspectral target detection (TD) algorithms. However, given the inherent spectral variability and subpixel problem in hyperspectral imagery (HSI), the target spectra obtained from a standard spectral library or pixels from images direc...
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Gespeichert in:
| Autor*in: |
Niu, Yubin [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2017 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: IEEE transactions on geoscience and remote sensing - New York, NY : IEEE, 1964, 55(2017), 3, Seite 1604-1617 |
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| Übergeordnetes Werk: |
volume:55 ; year:2017 ; number:3 ; pages:1604-1617 |
| Links: |
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| DOI / URN: |
10.1109/TGRS.2016.2628085 |
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OLC1992306389 |
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| 520 | |a The accuracy of target spectra determines the performances of hyperspectral target detection (TD) algorithms. However, given the inherent spectral variability and subpixel problem in hyperspectral imagery (HSI), the target spectra obtained from a standard spectral library or pixels from images directly are in most cases different from those of the real target spectra, resulting in low detection accuracy. The problem caused by inaccurate prior target information led to recognition of a new hotspot on HSI. In this paper, an adaptive weighted learning method (AWLM) using a self-completed background dictionary (SCBD) is specifically developed to extract the accurate target spectrum for hyperspectral TD. AWLM is derived from the idea of dictionary learning algorithms, learning the specific target spectrum with target-proportion-related adaptive weights. A strategy to construct SCBD is proposed to guarantee the convergence of AWLM to the accurate target spectrum. Utilizing the extracted target spectrum with higher accuracy, conventional TD algorithms can also achieve satisfactory detection results. Experimental results on both simulated and real hyperspectral data demonstrate that the proposed method has an advantage in extracting accurate target spectrum, enabling better and more robust detection results using conventional detectors than state-of-the-art methods that also aim at the problem of inaccurate prior target information of HSI. | ||
| 650 | 4 | |a Learning systems | |
| 650 | 4 | |a Detectors | |
| 650 | 4 | |a sparse coding | |
| 650 | 4 | |a Background dictionary | |
| 650 | 4 | |a Object detection | |
| 650 | 4 | |a Dictionaries | |
| 650 | 4 | |a hyperspectral imagery (HSI) | |
| 650 | 4 | |a Robustness | |
| 650 | 4 | |a Hyperspectral imaging | |
| 650 | 4 | |a learning method | |
| 650 | 4 | |a target detection (TD) | |
| 650 | 4 | |a spectral variability | |
| 650 | 4 | |a Image processing | |
| 650 | 4 | |a Artificial intelligence | |
| 650 | 4 | |a Self-organizing systems | |
| 650 | 4 | |a Research | |
| 650 | 4 | |a Usage | |
| 700 | 1 | |a Wang, Bin |4 oth | |
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10.1109/TGRS.2016.2628085 doi PQ20170721 (DE-627)OLC1992306389 (DE-599)GBVOLC1992306389 (PRQ)c1541-3cd6f21f8af69c2514a2924cf56adf08921a6c999d8ed3c48a0379bd7523ab080 (KEY)0048677920170000055000301604extractingtargetspectrumforhyperspectraltargetdete DE-627 ger DE-627 rakwb eng 620 550 DNB Niu, Yubin verfasserin aut Extracting Target Spectrum for Hyperspectral Target Detection: An Adaptive Weighted Learning Method Using a Self-Completed Background Dictionary 2017 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier The accuracy of target spectra determines the performances of hyperspectral target detection (TD) algorithms. However, given the inherent spectral variability and subpixel problem in hyperspectral imagery (HSI), the target spectra obtained from a standard spectral library or pixels from images directly are in most cases different from those of the real target spectra, resulting in low detection accuracy. The problem caused by inaccurate prior target information led to recognition of a new hotspot on HSI. In this paper, an adaptive weighted learning method (AWLM) using a self-completed background dictionary (SCBD) is specifically developed to extract the accurate target spectrum for hyperspectral TD. AWLM is derived from the idea of dictionary learning algorithms, learning the specific target spectrum with target-proportion-related adaptive weights. A strategy to construct SCBD is proposed to guarantee the convergence of AWLM to the accurate target spectrum. Utilizing the extracted target spectrum with higher accuracy, conventional TD algorithms can also achieve satisfactory detection results. Experimental results on both simulated and real hyperspectral data demonstrate that the proposed method has an advantage in extracting accurate target spectrum, enabling better and more robust detection results using conventional detectors than state-of-the-art methods that also aim at the problem of inaccurate prior target information of HSI. Learning systems Detectors sparse coding Background dictionary Object detection Dictionaries hyperspectral imagery (HSI) Robustness Hyperspectral imaging learning method target detection (TD) spectral variability Image processing Artificial intelligence Self-organizing systems Research Usage Wang, Bin oth Enthalten in IEEE transactions on geoscience and remote sensing New York, NY : IEEE, 1964 55(2017), 3, Seite 1604-1617 (DE-627)129601667 (DE-600)241439-9 (DE-576)015095282 0196-2892 nnns volume:55 year:2017 number:3 pages:1604-1617 http://dx.doi.org/10.1109/TGRS.2016.2628085 Volltext http://ieeexplore.ieee.org/document/7763810 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 AR 55 2017 3 1604-1617 |
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10.1109/TGRS.2016.2628085 doi PQ20170721 (DE-627)OLC1992306389 (DE-599)GBVOLC1992306389 (PRQ)c1541-3cd6f21f8af69c2514a2924cf56adf08921a6c999d8ed3c48a0379bd7523ab080 (KEY)0048677920170000055000301604extractingtargetspectrumforhyperspectraltargetdete DE-627 ger DE-627 rakwb eng 620 550 DNB Niu, Yubin verfasserin aut Extracting Target Spectrum for Hyperspectral Target Detection: An Adaptive Weighted Learning Method Using a Self-Completed Background Dictionary 2017 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier The accuracy of target spectra determines the performances of hyperspectral target detection (TD) algorithms. However, given the inherent spectral variability and subpixel problem in hyperspectral imagery (HSI), the target spectra obtained from a standard spectral library or pixels from images directly are in most cases different from those of the real target spectra, resulting in low detection accuracy. The problem caused by inaccurate prior target information led to recognition of a new hotspot on HSI. In this paper, an adaptive weighted learning method (AWLM) using a self-completed background dictionary (SCBD) is specifically developed to extract the accurate target spectrum for hyperspectral TD. AWLM is derived from the idea of dictionary learning algorithms, learning the specific target spectrum with target-proportion-related adaptive weights. A strategy to construct SCBD is proposed to guarantee the convergence of AWLM to the accurate target spectrum. Utilizing the extracted target spectrum with higher accuracy, conventional TD algorithms can also achieve satisfactory detection results. Experimental results on both simulated and real hyperspectral data demonstrate that the proposed method has an advantage in extracting accurate target spectrum, enabling better and more robust detection results using conventional detectors than state-of-the-art methods that also aim at the problem of inaccurate prior target information of HSI. Learning systems Detectors sparse coding Background dictionary Object detection Dictionaries hyperspectral imagery (HSI) Robustness Hyperspectral imaging learning method target detection (TD) spectral variability Image processing Artificial intelligence Self-organizing systems Research Usage Wang, Bin oth Enthalten in IEEE transactions on geoscience and remote sensing New York, NY : IEEE, 1964 55(2017), 3, Seite 1604-1617 (DE-627)129601667 (DE-600)241439-9 (DE-576)015095282 0196-2892 nnns volume:55 year:2017 number:3 pages:1604-1617 http://dx.doi.org/10.1109/TGRS.2016.2628085 Volltext http://ieeexplore.ieee.org/document/7763810 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 AR 55 2017 3 1604-1617 |
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10.1109/TGRS.2016.2628085 doi PQ20170721 (DE-627)OLC1992306389 (DE-599)GBVOLC1992306389 (PRQ)c1541-3cd6f21f8af69c2514a2924cf56adf08921a6c999d8ed3c48a0379bd7523ab080 (KEY)0048677920170000055000301604extractingtargetspectrumforhyperspectraltargetdete DE-627 ger DE-627 rakwb eng 620 550 DNB Niu, Yubin verfasserin aut Extracting Target Spectrum for Hyperspectral Target Detection: An Adaptive Weighted Learning Method Using a Self-Completed Background Dictionary 2017 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier The accuracy of target spectra determines the performances of hyperspectral target detection (TD) algorithms. However, given the inherent spectral variability and subpixel problem in hyperspectral imagery (HSI), the target spectra obtained from a standard spectral library or pixels from images directly are in most cases different from those of the real target spectra, resulting in low detection accuracy. The problem caused by inaccurate prior target information led to recognition of a new hotspot on HSI. In this paper, an adaptive weighted learning method (AWLM) using a self-completed background dictionary (SCBD) is specifically developed to extract the accurate target spectrum for hyperspectral TD. AWLM is derived from the idea of dictionary learning algorithms, learning the specific target spectrum with target-proportion-related adaptive weights. A strategy to construct SCBD is proposed to guarantee the convergence of AWLM to the accurate target spectrum. Utilizing the extracted target spectrum with higher accuracy, conventional TD algorithms can also achieve satisfactory detection results. Experimental results on both simulated and real hyperspectral data demonstrate that the proposed method has an advantage in extracting accurate target spectrum, enabling better and more robust detection results using conventional detectors than state-of-the-art methods that also aim at the problem of inaccurate prior target information of HSI. Learning systems Detectors sparse coding Background dictionary Object detection Dictionaries hyperspectral imagery (HSI) Robustness Hyperspectral imaging learning method target detection (TD) spectral variability Image processing Artificial intelligence Self-organizing systems Research Usage Wang, Bin oth Enthalten in IEEE transactions on geoscience and remote sensing New York, NY : IEEE, 1964 55(2017), 3, Seite 1604-1617 (DE-627)129601667 (DE-600)241439-9 (DE-576)015095282 0196-2892 nnns volume:55 year:2017 number:3 pages:1604-1617 http://dx.doi.org/10.1109/TGRS.2016.2628085 Volltext http://ieeexplore.ieee.org/document/7763810 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 AR 55 2017 3 1604-1617 |
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10.1109/TGRS.2016.2628085 doi PQ20170721 (DE-627)OLC1992306389 (DE-599)GBVOLC1992306389 (PRQ)c1541-3cd6f21f8af69c2514a2924cf56adf08921a6c999d8ed3c48a0379bd7523ab080 (KEY)0048677920170000055000301604extractingtargetspectrumforhyperspectraltargetdete DE-627 ger DE-627 rakwb eng 620 550 DNB Niu, Yubin verfasserin aut Extracting Target Spectrum for Hyperspectral Target Detection: An Adaptive Weighted Learning Method Using a Self-Completed Background Dictionary 2017 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier The accuracy of target spectra determines the performances of hyperspectral target detection (TD) algorithms. However, given the inherent spectral variability and subpixel problem in hyperspectral imagery (HSI), the target spectra obtained from a standard spectral library or pixels from images directly are in most cases different from those of the real target spectra, resulting in low detection accuracy. The problem caused by inaccurate prior target information led to recognition of a new hotspot on HSI. In this paper, an adaptive weighted learning method (AWLM) using a self-completed background dictionary (SCBD) is specifically developed to extract the accurate target spectrum for hyperspectral TD. AWLM is derived from the idea of dictionary learning algorithms, learning the specific target spectrum with target-proportion-related adaptive weights. A strategy to construct SCBD is proposed to guarantee the convergence of AWLM to the accurate target spectrum. Utilizing the extracted target spectrum with higher accuracy, conventional TD algorithms can also achieve satisfactory detection results. Experimental results on both simulated and real hyperspectral data demonstrate that the proposed method has an advantage in extracting accurate target spectrum, enabling better and more robust detection results using conventional detectors than state-of-the-art methods that also aim at the problem of inaccurate prior target information of HSI. Learning systems Detectors sparse coding Background dictionary Object detection Dictionaries hyperspectral imagery (HSI) Robustness Hyperspectral imaging learning method target detection (TD) spectral variability Image processing Artificial intelligence Self-organizing systems Research Usage Wang, Bin oth Enthalten in IEEE transactions on geoscience and remote sensing New York, NY : IEEE, 1964 55(2017), 3, Seite 1604-1617 (DE-627)129601667 (DE-600)241439-9 (DE-576)015095282 0196-2892 nnns volume:55 year:2017 number:3 pages:1604-1617 http://dx.doi.org/10.1109/TGRS.2016.2628085 Volltext http://ieeexplore.ieee.org/document/7763810 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 AR 55 2017 3 1604-1617 |
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10.1109/TGRS.2016.2628085 doi PQ20170721 (DE-627)OLC1992306389 (DE-599)GBVOLC1992306389 (PRQ)c1541-3cd6f21f8af69c2514a2924cf56adf08921a6c999d8ed3c48a0379bd7523ab080 (KEY)0048677920170000055000301604extractingtargetspectrumforhyperspectraltargetdete DE-627 ger DE-627 rakwb eng 620 550 DNB Niu, Yubin verfasserin aut Extracting Target Spectrum for Hyperspectral Target Detection: An Adaptive Weighted Learning Method Using a Self-Completed Background Dictionary 2017 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier The accuracy of target spectra determines the performances of hyperspectral target detection (TD) algorithms. However, given the inherent spectral variability and subpixel problem in hyperspectral imagery (HSI), the target spectra obtained from a standard spectral library or pixels from images directly are in most cases different from those of the real target spectra, resulting in low detection accuracy. The problem caused by inaccurate prior target information led to recognition of a new hotspot on HSI. In this paper, an adaptive weighted learning method (AWLM) using a self-completed background dictionary (SCBD) is specifically developed to extract the accurate target spectrum for hyperspectral TD. AWLM is derived from the idea of dictionary learning algorithms, learning the specific target spectrum with target-proportion-related adaptive weights. A strategy to construct SCBD is proposed to guarantee the convergence of AWLM to the accurate target spectrum. Utilizing the extracted target spectrum with higher accuracy, conventional TD algorithms can also achieve satisfactory detection results. Experimental results on both simulated and real hyperspectral data demonstrate that the proposed method has an advantage in extracting accurate target spectrum, enabling better and more robust detection results using conventional detectors than state-of-the-art methods that also aim at the problem of inaccurate prior target information of HSI. Learning systems Detectors sparse coding Background dictionary Object detection Dictionaries hyperspectral imagery (HSI) Robustness Hyperspectral imaging learning method target detection (TD) spectral variability Image processing Artificial intelligence Self-organizing systems Research Usage Wang, Bin oth Enthalten in IEEE transactions on geoscience and remote sensing New York, NY : IEEE, 1964 55(2017), 3, Seite 1604-1617 (DE-627)129601667 (DE-600)241439-9 (DE-576)015095282 0196-2892 nnns volume:55 year:2017 number:3 pages:1604-1617 http://dx.doi.org/10.1109/TGRS.2016.2628085 Volltext http://ieeexplore.ieee.org/document/7763810 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 AR 55 2017 3 1604-1617 |
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However, given the inherent spectral variability and subpixel problem in hyperspectral imagery (HSI), the target spectra obtained from a standard spectral library or pixels from images directly are in most cases different from those of the real target spectra, resulting in low detection accuracy. The problem caused by inaccurate prior target information led to recognition of a new hotspot on HSI. In this paper, an adaptive weighted learning method (AWLM) using a self-completed background dictionary (SCBD) is specifically developed to extract the accurate target spectrum for hyperspectral TD. AWLM is derived from the idea of dictionary learning algorithms, learning the specific target spectrum with target-proportion-related adaptive weights. A strategy to construct SCBD is proposed to guarantee the convergence of AWLM to the accurate target spectrum. Utilizing the extracted target spectrum with higher accuracy, conventional TD algorithms can also achieve satisfactory detection results. 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620 550 DNB Extracting Target Spectrum for Hyperspectral Target Detection: An Adaptive Weighted Learning Method Using a Self-Completed Background Dictionary Learning systems Detectors sparse coding Background dictionary Object detection Dictionaries hyperspectral imagery (HSI) Robustness Hyperspectral imaging learning method target detection (TD) spectral variability Image processing Artificial intelligence Self-organizing systems Research Usage |
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extracting target spectrum for hyperspectral target detection: an adaptive weighted learning method using a self-completed background dictionary |
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Extracting Target Spectrum for Hyperspectral Target Detection: An Adaptive Weighted Learning Method Using a Self-Completed Background Dictionary |
| abstract |
The accuracy of target spectra determines the performances of hyperspectral target detection (TD) algorithms. However, given the inherent spectral variability and subpixel problem in hyperspectral imagery (HSI), the target spectra obtained from a standard spectral library or pixels from images directly are in most cases different from those of the real target spectra, resulting in low detection accuracy. The problem caused by inaccurate prior target information led to recognition of a new hotspot on HSI. In this paper, an adaptive weighted learning method (AWLM) using a self-completed background dictionary (SCBD) is specifically developed to extract the accurate target spectrum for hyperspectral TD. AWLM is derived from the idea of dictionary learning algorithms, learning the specific target spectrum with target-proportion-related adaptive weights. A strategy to construct SCBD is proposed to guarantee the convergence of AWLM to the accurate target spectrum. Utilizing the extracted target spectrum with higher accuracy, conventional TD algorithms can also achieve satisfactory detection results. Experimental results on both simulated and real hyperspectral data demonstrate that the proposed method has an advantage in extracting accurate target spectrum, enabling better and more robust detection results using conventional detectors than state-of-the-art methods that also aim at the problem of inaccurate prior target information of HSI. |
| abstractGer |
The accuracy of target spectra determines the performances of hyperspectral target detection (TD) algorithms. However, given the inherent spectral variability and subpixel problem in hyperspectral imagery (HSI), the target spectra obtained from a standard spectral library or pixels from images directly are in most cases different from those of the real target spectra, resulting in low detection accuracy. The problem caused by inaccurate prior target information led to recognition of a new hotspot on HSI. In this paper, an adaptive weighted learning method (AWLM) using a self-completed background dictionary (SCBD) is specifically developed to extract the accurate target spectrum for hyperspectral TD. AWLM is derived from the idea of dictionary learning algorithms, learning the specific target spectrum with target-proportion-related adaptive weights. A strategy to construct SCBD is proposed to guarantee the convergence of AWLM to the accurate target spectrum. Utilizing the extracted target spectrum with higher accuracy, conventional TD algorithms can also achieve satisfactory detection results. Experimental results on both simulated and real hyperspectral data demonstrate that the proposed method has an advantage in extracting accurate target spectrum, enabling better and more robust detection results using conventional detectors than state-of-the-art methods that also aim at the problem of inaccurate prior target information of HSI. |
| abstract_unstemmed |
The accuracy of target spectra determines the performances of hyperspectral target detection (TD) algorithms. However, given the inherent spectral variability and subpixel problem in hyperspectral imagery (HSI), the target spectra obtained from a standard spectral library or pixels from images directly are in most cases different from those of the real target spectra, resulting in low detection accuracy. The problem caused by inaccurate prior target information led to recognition of a new hotspot on HSI. In this paper, an adaptive weighted learning method (AWLM) using a self-completed background dictionary (SCBD) is specifically developed to extract the accurate target spectrum for hyperspectral TD. AWLM is derived from the idea of dictionary learning algorithms, learning the specific target spectrum with target-proportion-related adaptive weights. A strategy to construct SCBD is proposed to guarantee the convergence of AWLM to the accurate target spectrum. Utilizing the extracted target spectrum with higher accuracy, conventional TD algorithms can also achieve satisfactory detection results. Experimental results on both simulated and real hyperspectral data demonstrate that the proposed method has an advantage in extracting accurate target spectrum, enabling better and more robust detection results using conventional detectors than state-of-the-art methods that also aim at the problem of inaccurate prior target information of HSI. |
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Extracting Target Spectrum for Hyperspectral Target Detection: An Adaptive Weighted Learning Method Using a Self-Completed Background Dictionary |
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