A New Cascade Model for the Hierarchical Joint Classification of Multitemporal and Multiresolution Remote Sensing Data
In this paper, we propose a novel method for the joint classification of both multidate and multiresolution remote sensing imagery, which represents an important and relatively unexplored classification problem. The proposed classifier is based on an explicit hierarchical graph-based model that is s...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Hedhli, Ihsen [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2016 |
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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, 54(2016), 11, Seite 6333-6348 |
|---|---|
| Übergeordnetes Werk: |
volume:54 ; year:2016 ; number:11 ; pages:6333-6348 |
| Links: |
|---|
| DOI / URN: |
10.1109/TGRS.2016.2580321 |
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| Katalog-ID: |
OLC1984601679 |
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| 520 | |a In this paper, we propose a novel method for the joint classification of both multidate and multiresolution remote sensing imagery, which represents an important and relatively unexplored classification problem. The proposed classifier is based on an explicit hierarchical graph-based model that is sufficiently flexible to address a coregistered time series of images collected at different spatial resolutions. Within this framework, a novel element of the proposed approach is the use of multiple quadtrees in cascade, each associated with the images available at each observation date in the considered time series. For each date, the input images are inserted in a hierarchical structure on the basis of their resolutions, whereas missing levels are filled in with wavelet transforms of the images embedded in finer-resolution levels. This approach is aimed at both exploiting multiscale information, which is known to play a crucial role in high-resolution image analysis, and supporting input images acquired at different resolutions in the input time series. The experimental results are shown for multitemporal and multiresolution optical data. | ||
| 650 | 4 | |a Spatial resolution | |
| 650 | 4 | |a multitemporal classification | |
| 650 | 4 | |a Remote sensing | |
| 650 | 4 | |a Hierarchical multiresolution Markov random fields | |
| 650 | 4 | |a Signal resolution | |
| 650 | 4 | |a Time series analysis | |
| 650 | 4 | |a satellite image time series | |
| 650 | 4 | |a Data models | |
| 650 | 4 | |a Simulated annealing | |
| 650 | 4 | |a Time series | |
| 650 | 4 | |a Wavelet transforms | |
| 700 | 1 | |a Moser, Gabriele |4 oth | |
| 700 | 1 | |a Zerubia, Josiane |4 oth | |
| 700 | 1 | |a Serpico, Sebastiano Bruno |4 oth | |
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10.1109/TGRS.2016.2580321 doi PQ20161201 (DE-627)OLC1984601679 (DE-599)GBVOLC1984601679 (PRQ)c1607-8f24e15407fbd17a3b981c9e933d290a426ba7e50f9473c488e8b835ff176dfe0 (KEY)0048677920160000054001106333newcascademodelforthehierarchicaljointclassificati DE-627 ger DE-627 rakwb eng 620 550 DNB Hedhli, Ihsen verfasserin aut A New Cascade Model for the Hierarchical Joint Classification of Multitemporal and Multiresolution Remote Sensing Data 2016 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier In this paper, we propose a novel method for the joint classification of both multidate and multiresolution remote sensing imagery, which represents an important and relatively unexplored classification problem. The proposed classifier is based on an explicit hierarchical graph-based model that is sufficiently flexible to address a coregistered time series of images collected at different spatial resolutions. Within this framework, a novel element of the proposed approach is the use of multiple quadtrees in cascade, each associated with the images available at each observation date in the considered time series. For each date, the input images are inserted in a hierarchical structure on the basis of their resolutions, whereas missing levels are filled in with wavelet transforms of the images embedded in finer-resolution levels. This approach is aimed at both exploiting multiscale information, which is known to play a crucial role in high-resolution image analysis, and supporting input images acquired at different resolutions in the input time series. The experimental results are shown for multitemporal and multiresolution optical data. Spatial resolution multitemporal classification Remote sensing Hierarchical multiresolution Markov random fields Signal resolution Time series analysis satellite image time series Data models Simulated annealing Time series Wavelet transforms Moser, Gabriele oth Zerubia, Josiane oth Serpico, Sebastiano Bruno oth Enthalten in IEEE transactions on geoscience and remote sensing New York, NY : IEEE, 1964 54(2016), 11, Seite 6333-6348 (DE-627)129601667 (DE-600)241439-9 (DE-576)015095282 0196-2892 nnns volume:54 year:2016 number:11 pages:6333-6348 http://dx.doi.org/10.1109/TGRS.2016.2580321 Volltext http://ieeexplore.ieee.org/document/7547331 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 54 2016 11 6333-6348 |
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10.1109/TGRS.2016.2580321 doi PQ20161201 (DE-627)OLC1984601679 (DE-599)GBVOLC1984601679 (PRQ)c1607-8f24e15407fbd17a3b981c9e933d290a426ba7e50f9473c488e8b835ff176dfe0 (KEY)0048677920160000054001106333newcascademodelforthehierarchicaljointclassificati DE-627 ger DE-627 rakwb eng 620 550 DNB Hedhli, Ihsen verfasserin aut A New Cascade Model for the Hierarchical Joint Classification of Multitemporal and Multiresolution Remote Sensing Data 2016 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier In this paper, we propose a novel method for the joint classification of both multidate and multiresolution remote sensing imagery, which represents an important and relatively unexplored classification problem. The proposed classifier is based on an explicit hierarchical graph-based model that is sufficiently flexible to address a coregistered time series of images collected at different spatial resolutions. Within this framework, a novel element of the proposed approach is the use of multiple quadtrees in cascade, each associated with the images available at each observation date in the considered time series. For each date, the input images are inserted in a hierarchical structure on the basis of their resolutions, whereas missing levels are filled in with wavelet transforms of the images embedded in finer-resolution levels. This approach is aimed at both exploiting multiscale information, which is known to play a crucial role in high-resolution image analysis, and supporting input images acquired at different resolutions in the input time series. The experimental results are shown for multitemporal and multiresolution optical data. Spatial resolution multitemporal classification Remote sensing Hierarchical multiresolution Markov random fields Signal resolution Time series analysis satellite image time series Data models Simulated annealing Time series Wavelet transforms Moser, Gabriele oth Zerubia, Josiane oth Serpico, Sebastiano Bruno oth Enthalten in IEEE transactions on geoscience and remote sensing New York, NY : IEEE, 1964 54(2016), 11, Seite 6333-6348 (DE-627)129601667 (DE-600)241439-9 (DE-576)015095282 0196-2892 nnns volume:54 year:2016 number:11 pages:6333-6348 http://dx.doi.org/10.1109/TGRS.2016.2580321 Volltext http://ieeexplore.ieee.org/document/7547331 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 54 2016 11 6333-6348 |
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10.1109/TGRS.2016.2580321 doi PQ20161201 (DE-627)OLC1984601679 (DE-599)GBVOLC1984601679 (PRQ)c1607-8f24e15407fbd17a3b981c9e933d290a426ba7e50f9473c488e8b835ff176dfe0 (KEY)0048677920160000054001106333newcascademodelforthehierarchicaljointclassificati DE-627 ger DE-627 rakwb eng 620 550 DNB Hedhli, Ihsen verfasserin aut A New Cascade Model for the Hierarchical Joint Classification of Multitemporal and Multiresolution Remote Sensing Data 2016 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier In this paper, we propose a novel method for the joint classification of both multidate and multiresolution remote sensing imagery, which represents an important and relatively unexplored classification problem. The proposed classifier is based on an explicit hierarchical graph-based model that is sufficiently flexible to address a coregistered time series of images collected at different spatial resolutions. Within this framework, a novel element of the proposed approach is the use of multiple quadtrees in cascade, each associated with the images available at each observation date in the considered time series. For each date, the input images are inserted in a hierarchical structure on the basis of their resolutions, whereas missing levels are filled in with wavelet transforms of the images embedded in finer-resolution levels. This approach is aimed at both exploiting multiscale information, which is known to play a crucial role in high-resolution image analysis, and supporting input images acquired at different resolutions in the input time series. The experimental results are shown for multitemporal and multiresolution optical data. Spatial resolution multitemporal classification Remote sensing Hierarchical multiresolution Markov random fields Signal resolution Time series analysis satellite image time series Data models Simulated annealing Time series Wavelet transforms Moser, Gabriele oth Zerubia, Josiane oth Serpico, Sebastiano Bruno oth Enthalten in IEEE transactions on geoscience and remote sensing New York, NY : IEEE, 1964 54(2016), 11, Seite 6333-6348 (DE-627)129601667 (DE-600)241439-9 (DE-576)015095282 0196-2892 nnns volume:54 year:2016 number:11 pages:6333-6348 http://dx.doi.org/10.1109/TGRS.2016.2580321 Volltext http://ieeexplore.ieee.org/document/7547331 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 54 2016 11 6333-6348 |
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10.1109/TGRS.2016.2580321 doi PQ20161201 (DE-627)OLC1984601679 (DE-599)GBVOLC1984601679 (PRQ)c1607-8f24e15407fbd17a3b981c9e933d290a426ba7e50f9473c488e8b835ff176dfe0 (KEY)0048677920160000054001106333newcascademodelforthehierarchicaljointclassificati DE-627 ger DE-627 rakwb eng 620 550 DNB Hedhli, Ihsen verfasserin aut A New Cascade Model for the Hierarchical Joint Classification of Multitemporal and Multiresolution Remote Sensing Data 2016 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier In this paper, we propose a novel method for the joint classification of both multidate and multiresolution remote sensing imagery, which represents an important and relatively unexplored classification problem. The proposed classifier is based on an explicit hierarchical graph-based model that is sufficiently flexible to address a coregistered time series of images collected at different spatial resolutions. Within this framework, a novel element of the proposed approach is the use of multiple quadtrees in cascade, each associated with the images available at each observation date in the considered time series. For each date, the input images are inserted in a hierarchical structure on the basis of their resolutions, whereas missing levels are filled in with wavelet transforms of the images embedded in finer-resolution levels. This approach is aimed at both exploiting multiscale information, which is known to play a crucial role in high-resolution image analysis, and supporting input images acquired at different resolutions in the input time series. The experimental results are shown for multitemporal and multiresolution optical data. Spatial resolution multitemporal classification Remote sensing Hierarchical multiresolution Markov random fields Signal resolution Time series analysis satellite image time series Data models Simulated annealing Time series Wavelet transforms Moser, Gabriele oth Zerubia, Josiane oth Serpico, Sebastiano Bruno oth Enthalten in IEEE transactions on geoscience and remote sensing New York, NY : IEEE, 1964 54(2016), 11, Seite 6333-6348 (DE-627)129601667 (DE-600)241439-9 (DE-576)015095282 0196-2892 nnns volume:54 year:2016 number:11 pages:6333-6348 http://dx.doi.org/10.1109/TGRS.2016.2580321 Volltext http://ieeexplore.ieee.org/document/7547331 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 54 2016 11 6333-6348 |
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10.1109/TGRS.2016.2580321 doi PQ20161201 (DE-627)OLC1984601679 (DE-599)GBVOLC1984601679 (PRQ)c1607-8f24e15407fbd17a3b981c9e933d290a426ba7e50f9473c488e8b835ff176dfe0 (KEY)0048677920160000054001106333newcascademodelforthehierarchicaljointclassificati DE-627 ger DE-627 rakwb eng 620 550 DNB Hedhli, Ihsen verfasserin aut A New Cascade Model for the Hierarchical Joint Classification of Multitemporal and Multiresolution Remote Sensing Data 2016 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier In this paper, we propose a novel method for the joint classification of both multidate and multiresolution remote sensing imagery, which represents an important and relatively unexplored classification problem. The proposed classifier is based on an explicit hierarchical graph-based model that is sufficiently flexible to address a coregistered time series of images collected at different spatial resolutions. Within this framework, a novel element of the proposed approach is the use of multiple quadtrees in cascade, each associated with the images available at each observation date in the considered time series. For each date, the input images are inserted in a hierarchical structure on the basis of their resolutions, whereas missing levels are filled in with wavelet transforms of the images embedded in finer-resolution levels. This approach is aimed at both exploiting multiscale information, which is known to play a crucial role in high-resolution image analysis, and supporting input images acquired at different resolutions in the input time series. The experimental results are shown for multitemporal and multiresolution optical data. Spatial resolution multitemporal classification Remote sensing Hierarchical multiresolution Markov random fields Signal resolution Time series analysis satellite image time series Data models Simulated annealing Time series Wavelet transforms Moser, Gabriele oth Zerubia, Josiane oth Serpico, Sebastiano Bruno oth Enthalten in IEEE transactions on geoscience and remote sensing New York, NY : IEEE, 1964 54(2016), 11, Seite 6333-6348 (DE-627)129601667 (DE-600)241439-9 (DE-576)015095282 0196-2892 nnns volume:54 year:2016 number:11 pages:6333-6348 http://dx.doi.org/10.1109/TGRS.2016.2580321 Volltext http://ieeexplore.ieee.org/document/7547331 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 54 2016 11 6333-6348 |
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620 550 DNB A New Cascade Model for the Hierarchical Joint Classification of Multitemporal and Multiresolution Remote Sensing Data Spatial resolution multitemporal classification Remote sensing Hierarchical multiresolution Markov random fields Signal resolution Time series analysis satellite image time series Data models Simulated annealing Time series Wavelet transforms |
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A New Cascade Model for the Hierarchical Joint Classification of Multitemporal and Multiresolution Remote Sensing Data |
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A New Cascade Model for the Hierarchical Joint Classification of Multitemporal and Multiresolution Remote Sensing Data |
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new cascade model for the hierarchical joint classification of multitemporal and multiresolution remote sensing data |
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A New Cascade Model for the Hierarchical Joint Classification of Multitemporal and Multiresolution Remote Sensing Data |
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In this paper, we propose a novel method for the joint classification of both multidate and multiresolution remote sensing imagery, which represents an important and relatively unexplored classification problem. The proposed classifier is based on an explicit hierarchical graph-based model that is sufficiently flexible to address a coregistered time series of images collected at different spatial resolutions. Within this framework, a novel element of the proposed approach is the use of multiple quadtrees in cascade, each associated with the images available at each observation date in the considered time series. For each date, the input images are inserted in a hierarchical structure on the basis of their resolutions, whereas missing levels are filled in with wavelet transforms of the images embedded in finer-resolution levels. This approach is aimed at both exploiting multiscale information, which is known to play a crucial role in high-resolution image analysis, and supporting input images acquired at different resolutions in the input time series. The experimental results are shown for multitemporal and multiresolution optical data. |
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In this paper, we propose a novel method for the joint classification of both multidate and multiresolution remote sensing imagery, which represents an important and relatively unexplored classification problem. The proposed classifier is based on an explicit hierarchical graph-based model that is sufficiently flexible to address a coregistered time series of images collected at different spatial resolutions. Within this framework, a novel element of the proposed approach is the use of multiple quadtrees in cascade, each associated with the images available at each observation date in the considered time series. For each date, the input images are inserted in a hierarchical structure on the basis of their resolutions, whereas missing levels are filled in with wavelet transforms of the images embedded in finer-resolution levels. This approach is aimed at both exploiting multiscale information, which is known to play a crucial role in high-resolution image analysis, and supporting input images acquired at different resolutions in the input time series. The experimental results are shown for multitemporal and multiresolution optical data. |
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In this paper, we propose a novel method for the joint classification of both multidate and multiresolution remote sensing imagery, which represents an important and relatively unexplored classification problem. The proposed classifier is based on an explicit hierarchical graph-based model that is sufficiently flexible to address a coregistered time series of images collected at different spatial resolutions. Within this framework, a novel element of the proposed approach is the use of multiple quadtrees in cascade, each associated with the images available at each observation date in the considered time series. For each date, the input images are inserted in a hierarchical structure on the basis of their resolutions, whereas missing levels are filled in with wavelet transforms of the images embedded in finer-resolution levels. This approach is aimed at both exploiting multiscale information, which is known to play a crucial role in high-resolution image analysis, and supporting input images acquired at different resolutions in the input time series. The experimental results are shown for multitemporal and multiresolution optical data. |
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A New Cascade Model for the Hierarchical Joint Classification of Multitemporal and Multiresolution Remote Sensing Data |
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