Sensitivity of Pansharpening Methods to Temporal and Instrumental Changes Between Multispectral and Panchromatic Data Sets
In this work, the authors investigate the behaviors of the two main classes of pansharpening methods: those based on component substitution (CS) or spectral methods and those based on multiresolution analysis (MRA) or spatial methods, in the presence of temporal and/or instrumental misalignments bet...
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| Autor*in: |
Aiazzi, Bruno [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2017 |
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| Übergeordnetes Werk: |
Enthalten in: IEEE transactions on geoscience and remote sensing - New York, NY : IEEE, 1964, 55(2017), 1, Seite 308-319 |
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| Übergeordnetes Werk: |
volume:55 ; year:2017 ; number:1 ; pages:308-319 |
| Links: |
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| DOI / URN: |
10.1109/TGRS.2016.2606324 |
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OLC1987670361 |
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| 520 | |a In this work, the authors investigate the behaviors of the two main classes of pansharpening methods: those based on component substitution (CS) or spectral methods and those based on multiresolution analysis (MRA) or spatial methods, in the presence of temporal and/or instrumental misalignments between the multispectral (MS) and panchromatic (Pan) data sets, that is, whenever MS and Pan are not jointly acquired at the same time and/or from the same platform. Starting from the mathematical formulation of CS and MRA pansharpening and from the spectral model between the Pan and MS channels, estimated through the multivariate linear regression between MS and spatially degraded Pan, it is proven that both CS and MRA methods may lose geometric sharpness in the case of a spectral mismatch, but spatial methods preserve the spectral diversity of the original MS data set regardless of the date or instrument of Pan image acquisition. Conversely, spectral methods also suffer from a loss of spectral fidelity to the original MS data set that is inversely related to the success of the spectral match between MS and Pan, measured by the coefficient of determination of the multivariate regression. An experimental setup exploiting GeoEye-1 and QuickBird data sets demonstrates the validity and intrinsic limitations of the proposed theoretical models. Depending of the target application, one class of methods may be preferred to another: Whenever spectral fidelity of pansharpened products to the original MS data sets is crucial, spectral methods should be avoided, and spatial methods are to be preferred. | ||
| 650 | 4 | |a multisensor systems | |
| 650 | 4 | |a remote sensing (RS) | |
| 650 | 4 | |a Instruments | |
| 650 | 4 | |a Optical sensors | |
| 650 | 4 | |a Spatial resolution | |
| 650 | 4 | |a Remote sensing | |
| 650 | 4 | |a Optical imaging | |
| 650 | 4 | |a optical transfer functions | |
| 650 | 4 | |a Multiresolution techniques | |
| 650 | 4 | |a Satellites | |
| 700 | 1 | |a Alparone, Luciano |4 oth | |
| 700 | 1 | |a Baronti, Stefano |4 oth | |
| 700 | 1 | |a Carla, Roberto |4 oth | |
| 700 | 1 | |a Garzelli, Andrea |4 oth | |
| 700 | 1 | |a Santurri, Leonardo |4 oth | |
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10.1109/TGRS.2016.2606324 doi PQ20170206 (DE-627)OLC1987670361 (DE-599)GBVOLC1987670361 (PRQ)c1248-62bbf836293c5faabc5f80ae8afcd87f50781b237b7a6b2982f3b5014b20604c0 (KEY)0048677920170000055000100308sensitivityofpansharpeningmethodstotemporalandinst DE-627 ger DE-627 rakwb eng 620 550 DNB Aiazzi, Bruno verfasserin aut Sensitivity of Pansharpening Methods to Temporal and Instrumental Changes Between Multispectral and Panchromatic Data Sets 2017 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier In this work, the authors investigate the behaviors of the two main classes of pansharpening methods: those based on component substitution (CS) or spectral methods and those based on multiresolution analysis (MRA) or spatial methods, in the presence of temporal and/or instrumental misalignments between the multispectral (MS) and panchromatic (Pan) data sets, that is, whenever MS and Pan are not jointly acquired at the same time and/or from the same platform. Starting from the mathematical formulation of CS and MRA pansharpening and from the spectral model between the Pan and MS channels, estimated through the multivariate linear regression between MS and spatially degraded Pan, it is proven that both CS and MRA methods may lose geometric sharpness in the case of a spectral mismatch, but spatial methods preserve the spectral diversity of the original MS data set regardless of the date or instrument of Pan image acquisition. Conversely, spectral methods also suffer from a loss of spectral fidelity to the original MS data set that is inversely related to the success of the spectral match between MS and Pan, measured by the coefficient of determination of the multivariate regression. An experimental setup exploiting GeoEye-1 and QuickBird data sets demonstrates the validity and intrinsic limitations of the proposed theoretical models. Depending of the target application, one class of methods may be preferred to another: Whenever spectral fidelity of pansharpened products to the original MS data sets is crucial, spectral methods should be avoided, and spatial methods are to be preferred. multisensor systems remote sensing (RS) Instruments Optical sensors Spatial resolution Remote sensing Optical imaging optical transfer functions Multiresolution techniques Satellites Alparone, Luciano oth Baronti, Stefano oth Carla, Roberto oth Garzelli, Andrea oth Santurri, Leonardo oth Enthalten in IEEE transactions on geoscience and remote sensing New York, NY : IEEE, 1964 55(2017), 1, Seite 308-319 (DE-627)129601667 (DE-600)241439-9 (DE-576)015095282 0196-2892 nnns volume:55 year:2017 number:1 pages:308-319 http://dx.doi.org/10.1109/TGRS.2016.2606324 Volltext http://ieeexplore.ieee.org/document/7572906 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 1 308-319 |
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10.1109/TGRS.2016.2606324 doi PQ20170206 (DE-627)OLC1987670361 (DE-599)GBVOLC1987670361 (PRQ)c1248-62bbf836293c5faabc5f80ae8afcd87f50781b237b7a6b2982f3b5014b20604c0 (KEY)0048677920170000055000100308sensitivityofpansharpeningmethodstotemporalandinst DE-627 ger DE-627 rakwb eng 620 550 DNB Aiazzi, Bruno verfasserin aut Sensitivity of Pansharpening Methods to Temporal and Instrumental Changes Between Multispectral and Panchromatic Data Sets 2017 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier In this work, the authors investigate the behaviors of the two main classes of pansharpening methods: those based on component substitution (CS) or spectral methods and those based on multiresolution analysis (MRA) or spatial methods, in the presence of temporal and/or instrumental misalignments between the multispectral (MS) and panchromatic (Pan) data sets, that is, whenever MS and Pan are not jointly acquired at the same time and/or from the same platform. Starting from the mathematical formulation of CS and MRA pansharpening and from the spectral model between the Pan and MS channels, estimated through the multivariate linear regression between MS and spatially degraded Pan, it is proven that both CS and MRA methods may lose geometric sharpness in the case of a spectral mismatch, but spatial methods preserve the spectral diversity of the original MS data set regardless of the date or instrument of Pan image acquisition. Conversely, spectral methods also suffer from a loss of spectral fidelity to the original MS data set that is inversely related to the success of the spectral match between MS and Pan, measured by the coefficient of determination of the multivariate regression. An experimental setup exploiting GeoEye-1 and QuickBird data sets demonstrates the validity and intrinsic limitations of the proposed theoretical models. Depending of the target application, one class of methods may be preferred to another: Whenever spectral fidelity of pansharpened products to the original MS data sets is crucial, spectral methods should be avoided, and spatial methods are to be preferred. multisensor systems remote sensing (RS) Instruments Optical sensors Spatial resolution Remote sensing Optical imaging optical transfer functions Multiresolution techniques Satellites Alparone, Luciano oth Baronti, Stefano oth Carla, Roberto oth Garzelli, Andrea oth Santurri, Leonardo oth Enthalten in IEEE transactions on geoscience and remote sensing New York, NY : IEEE, 1964 55(2017), 1, Seite 308-319 (DE-627)129601667 (DE-600)241439-9 (DE-576)015095282 0196-2892 nnns volume:55 year:2017 number:1 pages:308-319 http://dx.doi.org/10.1109/TGRS.2016.2606324 Volltext http://ieeexplore.ieee.org/document/7572906 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 1 308-319 |
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10.1109/TGRS.2016.2606324 doi PQ20170206 (DE-627)OLC1987670361 (DE-599)GBVOLC1987670361 (PRQ)c1248-62bbf836293c5faabc5f80ae8afcd87f50781b237b7a6b2982f3b5014b20604c0 (KEY)0048677920170000055000100308sensitivityofpansharpeningmethodstotemporalandinst DE-627 ger DE-627 rakwb eng 620 550 DNB Aiazzi, Bruno verfasserin aut Sensitivity of Pansharpening Methods to Temporal and Instrumental Changes Between Multispectral and Panchromatic Data Sets 2017 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier In this work, the authors investigate the behaviors of the two main classes of pansharpening methods: those based on component substitution (CS) or spectral methods and those based on multiresolution analysis (MRA) or spatial methods, in the presence of temporal and/or instrumental misalignments between the multispectral (MS) and panchromatic (Pan) data sets, that is, whenever MS and Pan are not jointly acquired at the same time and/or from the same platform. Starting from the mathematical formulation of CS and MRA pansharpening and from the spectral model between the Pan and MS channels, estimated through the multivariate linear regression between MS and spatially degraded Pan, it is proven that both CS and MRA methods may lose geometric sharpness in the case of a spectral mismatch, but spatial methods preserve the spectral diversity of the original MS data set regardless of the date or instrument of Pan image acquisition. Conversely, spectral methods also suffer from a loss of spectral fidelity to the original MS data set that is inversely related to the success of the spectral match between MS and Pan, measured by the coefficient of determination of the multivariate regression. An experimental setup exploiting GeoEye-1 and QuickBird data sets demonstrates the validity and intrinsic limitations of the proposed theoretical models. Depending of the target application, one class of methods may be preferred to another: Whenever spectral fidelity of pansharpened products to the original MS data sets is crucial, spectral methods should be avoided, and spatial methods are to be preferred. multisensor systems remote sensing (RS) Instruments Optical sensors Spatial resolution Remote sensing Optical imaging optical transfer functions Multiresolution techniques Satellites Alparone, Luciano oth Baronti, Stefano oth Carla, Roberto oth Garzelli, Andrea oth Santurri, Leonardo oth Enthalten in IEEE transactions on geoscience and remote sensing New York, NY : IEEE, 1964 55(2017), 1, Seite 308-319 (DE-627)129601667 (DE-600)241439-9 (DE-576)015095282 0196-2892 nnns volume:55 year:2017 number:1 pages:308-319 http://dx.doi.org/10.1109/TGRS.2016.2606324 Volltext http://ieeexplore.ieee.org/document/7572906 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 1 308-319 |
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10.1109/TGRS.2016.2606324 doi PQ20170206 (DE-627)OLC1987670361 (DE-599)GBVOLC1987670361 (PRQ)c1248-62bbf836293c5faabc5f80ae8afcd87f50781b237b7a6b2982f3b5014b20604c0 (KEY)0048677920170000055000100308sensitivityofpansharpeningmethodstotemporalandinst DE-627 ger DE-627 rakwb eng 620 550 DNB Aiazzi, Bruno verfasserin aut Sensitivity of Pansharpening Methods to Temporal and Instrumental Changes Between Multispectral and Panchromatic Data Sets 2017 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier In this work, the authors investigate the behaviors of the two main classes of pansharpening methods: those based on component substitution (CS) or spectral methods and those based on multiresolution analysis (MRA) or spatial methods, in the presence of temporal and/or instrumental misalignments between the multispectral (MS) and panchromatic (Pan) data sets, that is, whenever MS and Pan are not jointly acquired at the same time and/or from the same platform. Starting from the mathematical formulation of CS and MRA pansharpening and from the spectral model between the Pan and MS channels, estimated through the multivariate linear regression between MS and spatially degraded Pan, it is proven that both CS and MRA methods may lose geometric sharpness in the case of a spectral mismatch, but spatial methods preserve the spectral diversity of the original MS data set regardless of the date or instrument of Pan image acquisition. Conversely, spectral methods also suffer from a loss of spectral fidelity to the original MS data set that is inversely related to the success of the spectral match between MS and Pan, measured by the coefficient of determination of the multivariate regression. An experimental setup exploiting GeoEye-1 and QuickBird data sets demonstrates the validity and intrinsic limitations of the proposed theoretical models. Depending of the target application, one class of methods may be preferred to another: Whenever spectral fidelity of pansharpened products to the original MS data sets is crucial, spectral methods should be avoided, and spatial methods are to be preferred. multisensor systems remote sensing (RS) Instruments Optical sensors Spatial resolution Remote sensing Optical imaging optical transfer functions Multiresolution techniques Satellites Alparone, Luciano oth Baronti, Stefano oth Carla, Roberto oth Garzelli, Andrea oth Santurri, Leonardo oth Enthalten in IEEE transactions on geoscience and remote sensing New York, NY : IEEE, 1964 55(2017), 1, Seite 308-319 (DE-627)129601667 (DE-600)241439-9 (DE-576)015095282 0196-2892 nnns volume:55 year:2017 number:1 pages:308-319 http://dx.doi.org/10.1109/TGRS.2016.2606324 Volltext http://ieeexplore.ieee.org/document/7572906 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 1 308-319 |
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10.1109/TGRS.2016.2606324 doi PQ20170206 (DE-627)OLC1987670361 (DE-599)GBVOLC1987670361 (PRQ)c1248-62bbf836293c5faabc5f80ae8afcd87f50781b237b7a6b2982f3b5014b20604c0 (KEY)0048677920170000055000100308sensitivityofpansharpeningmethodstotemporalandinst DE-627 ger DE-627 rakwb eng 620 550 DNB Aiazzi, Bruno verfasserin aut Sensitivity of Pansharpening Methods to Temporal and Instrumental Changes Between Multispectral and Panchromatic Data Sets 2017 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier In this work, the authors investigate the behaviors of the two main classes of pansharpening methods: those based on component substitution (CS) or spectral methods and those based on multiresolution analysis (MRA) or spatial methods, in the presence of temporal and/or instrumental misalignments between the multispectral (MS) and panchromatic (Pan) data sets, that is, whenever MS and Pan are not jointly acquired at the same time and/or from the same platform. Starting from the mathematical formulation of CS and MRA pansharpening and from the spectral model between the Pan and MS channels, estimated through the multivariate linear regression between MS and spatially degraded Pan, it is proven that both CS and MRA methods may lose geometric sharpness in the case of a spectral mismatch, but spatial methods preserve the spectral diversity of the original MS data set regardless of the date or instrument of Pan image acquisition. Conversely, spectral methods also suffer from a loss of spectral fidelity to the original MS data set that is inversely related to the success of the spectral match between MS and Pan, measured by the coefficient of determination of the multivariate regression. An experimental setup exploiting GeoEye-1 and QuickBird data sets demonstrates the validity and intrinsic limitations of the proposed theoretical models. Depending of the target application, one class of methods may be preferred to another: Whenever spectral fidelity of pansharpened products to the original MS data sets is crucial, spectral methods should be avoided, and spatial methods are to be preferred. multisensor systems remote sensing (RS) Instruments Optical sensors Spatial resolution Remote sensing Optical imaging optical transfer functions Multiresolution techniques Satellites Alparone, Luciano oth Baronti, Stefano oth Carla, Roberto oth Garzelli, Andrea oth Santurri, Leonardo oth Enthalten in IEEE transactions on geoscience and remote sensing New York, NY : IEEE, 1964 55(2017), 1, Seite 308-319 (DE-627)129601667 (DE-600)241439-9 (DE-576)015095282 0196-2892 nnns volume:55 year:2017 number:1 pages:308-319 http://dx.doi.org/10.1109/TGRS.2016.2606324 Volltext http://ieeexplore.ieee.org/document/7572906 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 1 308-319 |
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Sensitivity of Pansharpening Methods to Temporal and Instrumental Changes Between Multispectral and Panchromatic Data Sets |
| abstract |
In this work, the authors investigate the behaviors of the two main classes of pansharpening methods: those based on component substitution (CS) or spectral methods and those based on multiresolution analysis (MRA) or spatial methods, in the presence of temporal and/or instrumental misalignments between the multispectral (MS) and panchromatic (Pan) data sets, that is, whenever MS and Pan are not jointly acquired at the same time and/or from the same platform. Starting from the mathematical formulation of CS and MRA pansharpening and from the spectral model between the Pan and MS channels, estimated through the multivariate linear regression between MS and spatially degraded Pan, it is proven that both CS and MRA methods may lose geometric sharpness in the case of a spectral mismatch, but spatial methods preserve the spectral diversity of the original MS data set regardless of the date or instrument of Pan image acquisition. Conversely, spectral methods also suffer from a loss of spectral fidelity to the original MS data set that is inversely related to the success of the spectral match between MS and Pan, measured by the coefficient of determination of the multivariate regression. An experimental setup exploiting GeoEye-1 and QuickBird data sets demonstrates the validity and intrinsic limitations of the proposed theoretical models. Depending of the target application, one class of methods may be preferred to another: Whenever spectral fidelity of pansharpened products to the original MS data sets is crucial, spectral methods should be avoided, and spatial methods are to be preferred. |
| abstractGer |
In this work, the authors investigate the behaviors of the two main classes of pansharpening methods: those based on component substitution (CS) or spectral methods and those based on multiresolution analysis (MRA) or spatial methods, in the presence of temporal and/or instrumental misalignments between the multispectral (MS) and panchromatic (Pan) data sets, that is, whenever MS and Pan are not jointly acquired at the same time and/or from the same platform. Starting from the mathematical formulation of CS and MRA pansharpening and from the spectral model between the Pan and MS channels, estimated through the multivariate linear regression between MS and spatially degraded Pan, it is proven that both CS and MRA methods may lose geometric sharpness in the case of a spectral mismatch, but spatial methods preserve the spectral diversity of the original MS data set regardless of the date or instrument of Pan image acquisition. Conversely, spectral methods also suffer from a loss of spectral fidelity to the original MS data set that is inversely related to the success of the spectral match between MS and Pan, measured by the coefficient of determination of the multivariate regression. An experimental setup exploiting GeoEye-1 and QuickBird data sets demonstrates the validity and intrinsic limitations of the proposed theoretical models. Depending of the target application, one class of methods may be preferred to another: Whenever spectral fidelity of pansharpened products to the original MS data sets is crucial, spectral methods should be avoided, and spatial methods are to be preferred. |
| abstract_unstemmed |
In this work, the authors investigate the behaviors of the two main classes of pansharpening methods: those based on component substitution (CS) or spectral methods and those based on multiresolution analysis (MRA) or spatial methods, in the presence of temporal and/or instrumental misalignments between the multispectral (MS) and panchromatic (Pan) data sets, that is, whenever MS and Pan are not jointly acquired at the same time and/or from the same platform. Starting from the mathematical formulation of CS and MRA pansharpening and from the spectral model between the Pan and MS channels, estimated through the multivariate linear regression between MS and spatially degraded Pan, it is proven that both CS and MRA methods may lose geometric sharpness in the case of a spectral mismatch, but spatial methods preserve the spectral diversity of the original MS data set regardless of the date or instrument of Pan image acquisition. Conversely, spectral methods also suffer from a loss of spectral fidelity to the original MS data set that is inversely related to the success of the spectral match between MS and Pan, measured by the coefficient of determination of the multivariate regression. An experimental setup exploiting GeoEye-1 and QuickBird data sets demonstrates the validity and intrinsic limitations of the proposed theoretical models. Depending of the target application, one class of methods may be preferred to another: Whenever spectral fidelity of pansharpened products to the original MS data sets is crucial, spectral methods should be avoided, and spatial methods are to be preferred. |
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Sensitivity of Pansharpening Methods to Temporal and Instrumental Changes Between Multispectral and Panchromatic Data Sets |
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