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Sinopsis
The novelty of this algorithm is to apply multiple sources of remote sensing data combined with data of unmanned weather stations, topography, ground cover,DEM, and astronomy and calendar rules. The results indicated that the model has high accuracy, reliability, and generalization ability. Factors such as cloudiness, ground vegetation, and water vapor show little interference, so the model seems suitable for large area retrieving under natural conditions. The required high-performance computation was achieved by a CPU + GPU isomery and synergy parallel computation system that improved computing speed by more than 1000-fold, with easily extendable computing capability. We found that the current algorithm is superior to seven major split-window algorithms and their best combined algorithms based on prediction errors, root-meansquare errors, and the percentage of data points with <3 ◦C absolute error. Our SVM approach overcomes shortcomings of classical temperature remote sensing technologies, and is the first report of such application.
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His research interests include multisource remote sensing image processing,GIS & GIS system developing, high-performance computation (HPC) and its application in processing RS image, support vector machine (SVM) algorithms and its merging into GIS system, and scalability of image processing for large remote sensing image with HPC.
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- EditorialLAP LAMBERT Academic Publishing
- Año de publicación2016
- ISBN 10 3659978205
- ISBN 13 9783659978203
- EncuadernaciónTapa blanda
- IdiomaInglés
- Número de páginas84
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Remote Sensing, Nonlinear Model, Supercomputing
Publicado por
LAP LAMBERT Academic Publishing Nov 2016, 2016
ISBN 10: 3659978205
ISBN 13: 9783659978203
Nuevo
Taschenbuch
Librería: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Alemania
Calificación del vendedor: 5 de 5 estrellas
Taschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -The novelty of this algorithm is to apply multiple sources of remote sensing data combined with data of unmanned weather stations, topography, ground cover,DEM, and astronomy and calendar rules. The results indicated that the model has high accuracy, reliability, and generalization ability. Factors such as cloudiness, ground vegetation, and water vapor show little interference, so the model seems suitable for large area retrieving under natural conditions. The required high-performance computation was achieved by a CPU + GPU isomery and synergy parallel computation system that improved computing speed by more than 1000-fold, with easily extendable computing capability. We found that the current algorithm is superior to seven major split-window algorithms and their best combined algorithms based on prediction errors, root-meansquare errors, and the percentage of data points with 3 C absolute error. Our SVM approach overcomes shortcomings of classical temperature remote sensing technologies, and is the first report of such application. 84 pp. Englisch. Nº de ref. del artículo: 9783659978203
Cantidad disponible: 2 disponibles
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Remote Sensing, Nonlinear Model, Supercomputing
Publicado por
LAP LAMBERT Academic Publishing, 2016
ISBN 10: 3659978205
ISBN 13: 9783659978203
Nuevo
Taschenbuch
Librería: AHA-BUCH GmbH, Einbeck, Alemania
Calificación del vendedor: 5 de 5 estrellas
Taschenbuch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - The novelty of this algorithm is to apply multiple sources of remote sensing data combined with data of unmanned weather stations, topography, ground cover,DEM, and astronomy and calendar rules. The results indicated that the model has high accuracy, reliability, and generalization ability. Factors such as cloudiness, ground vegetation, and water vapor show little interference, so the model seems suitable for large area retrieving under natural conditions. The required high-performance computation was achieved by a CPU + GPU isomery and synergy parallel computation system that improved computing speed by more than 1000-fold, with easily extendable computing capability. We found that the current algorithm is superior to seven major split-window algorithms and their best combined algorithms based on prediction errors, root-meansquare errors, and the percentage of data points with 3 C absolute error. Our SVM approach overcomes shortcomings of classical temperature remote sensing technologies, and is the first report of such application. Nº de ref. del artículo: 9783659978203
Cantidad disponible: 1 disponibles
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Remote Sensing, Nonlinear Model, Supercomputing
Publicado por
LAP LAMBERT Academic Publishing, 2016
ISBN 10: 3659978205
ISBN 13: 9783659978203
Nuevo
Tapa blanda
Librería: moluna, Greven, Alemania
Calificación del vendedor: 5 de 5 estrellas
Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Qin Jiang LinHis research interests include multisource remote sensing image processing,GIS & GIS system developing, high-performance computation (HPC) and its application in processing RS image, support vector machine (SVM) algorith. Nº de ref. del artículo: 158964221
Cantidad disponible: Más de 20 disponibles
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Remote Sensing, Nonlinear Model, Supercomputing
Publicado por
LAP LAMBERT Academic Publishing, 2016
ISBN 10: 3659978205
ISBN 13: 9783659978203
Nuevo
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Librería: Revaluation Books, Exeter, Reino Unido
Calificación del vendedor: 5 de 5 estrellas
Paperback. Condición: Brand New. 84 pages. 8.66x5.91x0.19 inches. In Stock. Nº de ref. del artículo: 3659978205
Cantidad disponible: 1 disponibles
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Remote Sensing, Nonlinear Model, Supercomputing
Publicado por
LAP LAMBERT Academic Publishing Nov 2016, 2016
ISBN 10: 3659978205
ISBN 13: 9783659978203
Nuevo
Taschenbuch
Librería: buchversandmimpf2000, Emtmannsberg, BAYE, Alemania
Calificación del vendedor: 5 de 5 estrellas
Taschenbuch. Condición: Neu. Neuware -The novelty of this algorithm is to apply multiple sources of remote sensing data combined with data of unmanned weather stations, topography, ground cover,DEM, and astronomy and calendar rules. The results indicated that the model has high accuracy, reliability, and generalization ability. Factors such as cloudiness, ground vegetation, and water vapor show little interference, so the model seems suitable for large area retrieving under natural conditions. The required high-performance computation was achieved by a CPU + GPU isomery and synergy parallel computation system that improved computing speed by more than 1000-fold, with easily extendable computing capability. We found that the current algorithm is superior to seven major split-window algorithms and their best combined algorithms based on prediction errors, root-meansquare errors, and the percentage of data points withBooks on Demand GmbH, Überseering 33, 22297 Hamburg 84 pp. Englisch. Nº de ref. del artículo: 9783659978203
Cantidad disponible: 2 disponibles