An SMP soft classification algorithm for remote sensing
This work introduces a symmetric multiprocessing (SMP) version of the continuous iterative guided spectral class rejection (CIGSCR) algorithm, a semiautomated classification algorithm for remote sensing (multispectral) images. The algorithm uses soft data clusters to produce a soft classification co...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Phillips, Rhonda D. [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2014transfer abstract |
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| Schlagwörter: |
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| Umfang: |
8 |
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| Übergeordnetes Werk: |
Enthalten in: Ultrafast acquirement of combined time and frequency spectroscopic data - 2012transfer abstract, an international journal devoted to the publication of papers on all aspects of geocomputation and to the distribution of computer programs and test data sets : an official journal of the International Association for Mathematical Geology, Amsterdam [u.a.] |
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| Übergeordnetes Werk: |
volume:68 ; year:2014 ; pages:73-80 ; extent:8 |
| Links: |
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| DOI / URN: |
10.1016/j.cageo.2014.03.010 |
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| 520 | |a This work introduces a symmetric multiprocessing (SMP) version of the continuous iterative guided spectral class rejection (CIGSCR) algorithm, a semiautomated classification algorithm for remote sensing (multispectral) images. The algorithm uses soft data clusters to produce a soft classification containing inherently more information than a comparable hard classification at an increased computational cost. Previous work suggests that similar algorithms achieve good parallel scalability, motivating the parallel algorithm development work here. Experimental results of applying parallel CIGSCR to an image with approximately 108 pixels and six bands demonstrate superlinear speedup. A soft two class classification is generated in just over 4min using 32 processors. | ||
| 520 | |a This work introduces a symmetric multiprocessing (SMP) version of the continuous iterative guided spectral class rejection (CIGSCR) algorithm, a semiautomated classification algorithm for remote sensing (multispectral) images. The algorithm uses soft data clusters to produce a soft classification containing inherently more information than a comparable hard classification at an increased computational cost. Previous work suggests that similar algorithms achieve good parallel scalability, motivating the parallel algorithm development work here. Experimental results of applying parallel CIGSCR to an image with approximately 108 pixels and six bands demonstrate superlinear speedup. A soft two class classification is generated in just over 4min using 32 processors. | ||
| 650 | 7 | |a Semisupervised clustering |2 Elsevier | |
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| 650 | 7 | |a Classification |2 Elsevier | |
| 650 | 7 | |a Remote sensing |2 Elsevier | |
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| 700 | 1 | |a Easterling, David R. |4 oth | |
| 700 | 1 | |a Wynne, Randolph H. |4 oth | |
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10.1016/j.cageo.2014.03.010 doi GBVA2014002000007.pica (DE-627)ELV033640068 (ELSEVIER)S0098-3004(14)00060-0 DE-627 ger DE-627 rakwb eng 550 004 550 DE-600 004 DE-600 530 VZ 580 VZ AFRIKA DE-30 fid BIODIV DE-30 fid 42.38 bkl Phillips, Rhonda D. verfasserin aut An SMP soft classification algorithm for remote sensing 2014transfer abstract 8 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier This work introduces a symmetric multiprocessing (SMP) version of the continuous iterative guided spectral class rejection (CIGSCR) algorithm, a semiautomated classification algorithm for remote sensing (multispectral) images. The algorithm uses soft data clusters to produce a soft classification containing inherently more information than a comparable hard classification at an increased computational cost. Previous work suggests that similar algorithms achieve good parallel scalability, motivating the parallel algorithm development work here. Experimental results of applying parallel CIGSCR to an image with approximately 108 pixels and six bands demonstrate superlinear speedup. A soft two class classification is generated in just over 4min using 32 processors. This work introduces a symmetric multiprocessing (SMP) version of the continuous iterative guided spectral class rejection (CIGSCR) algorithm, a semiautomated classification algorithm for remote sensing (multispectral) images. The algorithm uses soft data clusters to produce a soft classification containing inherently more information than a comparable hard classification at an increased computational cost. Previous work suggests that similar algorithms achieve good parallel scalability, motivating the parallel algorithm development work here. Experimental results of applying parallel CIGSCR to an image with approximately 108 pixels and six bands demonstrate superlinear speedup. A soft two class classification is generated in just over 4min using 32 processors. Semisupervised clustering Elsevier IGSCR Elsevier Classification Elsevier Remote sensing Elsevier Watson, Layne T. oth Easterling, David R. oth Wynne, Randolph H. oth Enthalten in Elsevier Science Ultrafast acquirement of combined time and frequency spectroscopic data 2012transfer abstract an international journal devoted to the publication of papers on all aspects of geocomputation and to the distribution of computer programs and test data sets : an official journal of the International Association for Mathematical Geology Amsterdam [u.a.] (DE-627)ELV021566380 volume:68 year:2014 pages:73-80 extent:8 https://doi.org/10.1016/j.cageo.2014.03.010 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-AFRIKA FID-BIODIV GBV_ILN_21 GBV_ILN_22 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_130 42.38 Botanik: Allgemeines VZ AR 68 2014 73-80 8 045F 550 |
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10.1016/j.cageo.2014.03.010 doi GBVA2014002000007.pica (DE-627)ELV033640068 (ELSEVIER)S0098-3004(14)00060-0 DE-627 ger DE-627 rakwb eng 550 004 550 DE-600 004 DE-600 530 VZ 580 VZ AFRIKA DE-30 fid BIODIV DE-30 fid 42.38 bkl Phillips, Rhonda D. verfasserin aut An SMP soft classification algorithm for remote sensing 2014transfer abstract 8 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier This work introduces a symmetric multiprocessing (SMP) version of the continuous iterative guided spectral class rejection (CIGSCR) algorithm, a semiautomated classification algorithm for remote sensing (multispectral) images. The algorithm uses soft data clusters to produce a soft classification containing inherently more information than a comparable hard classification at an increased computational cost. Previous work suggests that similar algorithms achieve good parallel scalability, motivating the parallel algorithm development work here. Experimental results of applying parallel CIGSCR to an image with approximately 108 pixels and six bands demonstrate superlinear speedup. A soft two class classification is generated in just over 4min using 32 processors. This work introduces a symmetric multiprocessing (SMP) version of the continuous iterative guided spectral class rejection (CIGSCR) algorithm, a semiautomated classification algorithm for remote sensing (multispectral) images. The algorithm uses soft data clusters to produce a soft classification containing inherently more information than a comparable hard classification at an increased computational cost. Previous work suggests that similar algorithms achieve good parallel scalability, motivating the parallel algorithm development work here. Experimental results of applying parallel CIGSCR to an image with approximately 108 pixels and six bands demonstrate superlinear speedup. A soft two class classification is generated in just over 4min using 32 processors. Semisupervised clustering Elsevier IGSCR Elsevier Classification Elsevier Remote sensing Elsevier Watson, Layne T. oth Easterling, David R. oth Wynne, Randolph H. oth Enthalten in Elsevier Science Ultrafast acquirement of combined time and frequency spectroscopic data 2012transfer abstract an international journal devoted to the publication of papers on all aspects of geocomputation and to the distribution of computer programs and test data sets : an official journal of the International Association for Mathematical Geology Amsterdam [u.a.] (DE-627)ELV021566380 volume:68 year:2014 pages:73-80 extent:8 https://doi.org/10.1016/j.cageo.2014.03.010 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-AFRIKA FID-BIODIV GBV_ILN_21 GBV_ILN_22 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_130 42.38 Botanik: Allgemeines VZ AR 68 2014 73-80 8 045F 550 |
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10.1016/j.cageo.2014.03.010 doi GBVA2014002000007.pica (DE-627)ELV033640068 (ELSEVIER)S0098-3004(14)00060-0 DE-627 ger DE-627 rakwb eng 550 004 550 DE-600 004 DE-600 530 VZ 580 VZ AFRIKA DE-30 fid BIODIV DE-30 fid 42.38 bkl Phillips, Rhonda D. verfasserin aut An SMP soft classification algorithm for remote sensing 2014transfer abstract 8 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier This work introduces a symmetric multiprocessing (SMP) version of the continuous iterative guided spectral class rejection (CIGSCR) algorithm, a semiautomated classification algorithm for remote sensing (multispectral) images. The algorithm uses soft data clusters to produce a soft classification containing inherently more information than a comparable hard classification at an increased computational cost. Previous work suggests that similar algorithms achieve good parallel scalability, motivating the parallel algorithm development work here. Experimental results of applying parallel CIGSCR to an image with approximately 108 pixels and six bands demonstrate superlinear speedup. A soft two class classification is generated in just over 4min using 32 processors. This work introduces a symmetric multiprocessing (SMP) version of the continuous iterative guided spectral class rejection (CIGSCR) algorithm, a semiautomated classification algorithm for remote sensing (multispectral) images. The algorithm uses soft data clusters to produce a soft classification containing inherently more information than a comparable hard classification at an increased computational cost. Previous work suggests that similar algorithms achieve good parallel scalability, motivating the parallel algorithm development work here. Experimental results of applying parallel CIGSCR to an image with approximately 108 pixels and six bands demonstrate superlinear speedup. A soft two class classification is generated in just over 4min using 32 processors. Semisupervised clustering Elsevier IGSCR Elsevier Classification Elsevier Remote sensing Elsevier Watson, Layne T. oth Easterling, David R. oth Wynne, Randolph H. oth Enthalten in Elsevier Science Ultrafast acquirement of combined time and frequency spectroscopic data 2012transfer abstract an international journal devoted to the publication of papers on all aspects of geocomputation and to the distribution of computer programs and test data sets : an official journal of the International Association for Mathematical Geology Amsterdam [u.a.] (DE-627)ELV021566380 volume:68 year:2014 pages:73-80 extent:8 https://doi.org/10.1016/j.cageo.2014.03.010 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-AFRIKA FID-BIODIV GBV_ILN_21 GBV_ILN_22 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_130 42.38 Botanik: Allgemeines VZ AR 68 2014 73-80 8 045F 550 |
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10.1016/j.cageo.2014.03.010 doi GBVA2014002000007.pica (DE-627)ELV033640068 (ELSEVIER)S0098-3004(14)00060-0 DE-627 ger DE-627 rakwb eng 550 004 550 DE-600 004 DE-600 530 VZ 580 VZ AFRIKA DE-30 fid BIODIV DE-30 fid 42.38 bkl Phillips, Rhonda D. verfasserin aut An SMP soft classification algorithm for remote sensing 2014transfer abstract 8 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier This work introduces a symmetric multiprocessing (SMP) version of the continuous iterative guided spectral class rejection (CIGSCR) algorithm, a semiautomated classification algorithm for remote sensing (multispectral) images. The algorithm uses soft data clusters to produce a soft classification containing inherently more information than a comparable hard classification at an increased computational cost. Previous work suggests that similar algorithms achieve good parallel scalability, motivating the parallel algorithm development work here. Experimental results of applying parallel CIGSCR to an image with approximately 108 pixels and six bands demonstrate superlinear speedup. A soft two class classification is generated in just over 4min using 32 processors. This work introduces a symmetric multiprocessing (SMP) version of the continuous iterative guided spectral class rejection (CIGSCR) algorithm, a semiautomated classification algorithm for remote sensing (multispectral) images. The algorithm uses soft data clusters to produce a soft classification containing inherently more information than a comparable hard classification at an increased computational cost. Previous work suggests that similar algorithms achieve good parallel scalability, motivating the parallel algorithm development work here. Experimental results of applying parallel CIGSCR to an image with approximately 108 pixels and six bands demonstrate superlinear speedup. A soft two class classification is generated in just over 4min using 32 processors. Semisupervised clustering Elsevier IGSCR Elsevier Classification Elsevier Remote sensing Elsevier Watson, Layne T. oth Easterling, David R. oth Wynne, Randolph H. oth Enthalten in Elsevier Science Ultrafast acquirement of combined time and frequency spectroscopic data 2012transfer abstract an international journal devoted to the publication of papers on all aspects of geocomputation and to the distribution of computer programs and test data sets : an official journal of the International Association for Mathematical Geology Amsterdam [u.a.] (DE-627)ELV021566380 volume:68 year:2014 pages:73-80 extent:8 https://doi.org/10.1016/j.cageo.2014.03.010 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-AFRIKA FID-BIODIV GBV_ILN_21 GBV_ILN_22 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_130 42.38 Botanik: Allgemeines VZ AR 68 2014 73-80 8 045F 550 |
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10.1016/j.cageo.2014.03.010 doi GBVA2014002000007.pica (DE-627)ELV033640068 (ELSEVIER)S0098-3004(14)00060-0 DE-627 ger DE-627 rakwb eng 550 004 550 DE-600 004 DE-600 530 VZ 580 VZ AFRIKA DE-30 fid BIODIV DE-30 fid 42.38 bkl Phillips, Rhonda D. verfasserin aut An SMP soft classification algorithm for remote sensing 2014transfer abstract 8 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier This work introduces a symmetric multiprocessing (SMP) version of the continuous iterative guided spectral class rejection (CIGSCR) algorithm, a semiautomated classification algorithm for remote sensing (multispectral) images. The algorithm uses soft data clusters to produce a soft classification containing inherently more information than a comparable hard classification at an increased computational cost. Previous work suggests that similar algorithms achieve good parallel scalability, motivating the parallel algorithm development work here. Experimental results of applying parallel CIGSCR to an image with approximately 108 pixels and six bands demonstrate superlinear speedup. A soft two class classification is generated in just over 4min using 32 processors. This work introduces a symmetric multiprocessing (SMP) version of the continuous iterative guided spectral class rejection (CIGSCR) algorithm, a semiautomated classification algorithm for remote sensing (multispectral) images. The algorithm uses soft data clusters to produce a soft classification containing inherently more information than a comparable hard classification at an increased computational cost. Previous work suggests that similar algorithms achieve good parallel scalability, motivating the parallel algorithm development work here. Experimental results of applying parallel CIGSCR to an image with approximately 108 pixels and six bands demonstrate superlinear speedup. A soft two class classification is generated in just over 4min using 32 processors. Semisupervised clustering Elsevier IGSCR Elsevier Classification Elsevier Remote sensing Elsevier Watson, Layne T. oth Easterling, David R. oth Wynne, Randolph H. oth Enthalten in Elsevier Science Ultrafast acquirement of combined time and frequency spectroscopic data 2012transfer abstract an international journal devoted to the publication of papers on all aspects of geocomputation and to the distribution of computer programs and test data sets : an official journal of the International Association for Mathematical Geology Amsterdam [u.a.] (DE-627)ELV021566380 volume:68 year:2014 pages:73-80 extent:8 https://doi.org/10.1016/j.cageo.2014.03.010 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-AFRIKA FID-BIODIV GBV_ILN_21 GBV_ILN_22 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_130 42.38 Botanik: Allgemeines VZ AR 68 2014 73-80 8 045F 550 |
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Enthalten in Ultrafast acquirement of combined time and frequency spectroscopic data Amsterdam [u.a.] volume:68 year:2014 pages:73-80 extent:8 |
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Ultrafast acquirement of combined time and frequency spectroscopic data |
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Phillips, Rhonda D. @@aut@@ Watson, Layne T. @@oth@@ Easterling, David R. @@oth@@ Wynne, Randolph H. @@oth@@ |
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This work introduces a symmetric multiprocessing (SMP) version of the continuous iterative guided spectral class rejection (CIGSCR) algorithm, a semiautomated classification algorithm for remote sensing (multispectral) images. The algorithm uses soft data clusters to produce a soft classification containing inherently more information than a comparable hard classification at an increased computational cost. Previous work suggests that similar algorithms achieve good parallel scalability, motivating the parallel algorithm development work here. Experimental results of applying parallel CIGSCR to an image with approximately 108 pixels and six bands demonstrate superlinear speedup. A soft two class classification is generated in just over 4min using 32 processors. |
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This work introduces a symmetric multiprocessing (SMP) version of the continuous iterative guided spectral class rejection (CIGSCR) algorithm, a semiautomated classification algorithm for remote sensing (multispectral) images. The algorithm uses soft data clusters to produce a soft classification containing inherently more information than a comparable hard classification at an increased computational cost. Previous work suggests that similar algorithms achieve good parallel scalability, motivating the parallel algorithm development work here. Experimental results of applying parallel CIGSCR to an image with approximately 108 pixels and six bands demonstrate superlinear speedup. A soft two class classification is generated in just over 4min using 32 processors. |
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This work introduces a symmetric multiprocessing (SMP) version of the continuous iterative guided spectral class rejection (CIGSCR) algorithm, a semiautomated classification algorithm for remote sensing (multispectral) images. The algorithm uses soft data clusters to produce a soft classification containing inherently more information than a comparable hard classification at an increased computational cost. Previous work suggests that similar algorithms achieve good parallel scalability, motivating the parallel algorithm development work here. Experimental results of applying parallel CIGSCR to an image with approximately 108 pixels and six bands demonstrate superlinear speedup. A soft two class classification is generated in just over 4min using 32 processors. |
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An SMP soft classification algorithm for remote sensing |
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