Articoli correlati a Remote Sensing, Nonlinear Model, Supercomputing

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9783659978203: Remote Sensing, Nonlinear Model, Supercomputing

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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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Informazioni sull?autore

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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  • EditoreLAP LAMBERT Academic Publishing
  • Data di pubblicazione2016
  • ISBN 10 3659978205
  • ISBN 13 9783659978203
  • RilegaturaCopertina flessibile
  • LinguaInglese
  • Numero di pagine84
  • Contatto del produttorenon disponibile

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Jiang Lin Qin
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Condizione: 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. Codice articolo 158964221

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Jiang Lin Qin
ISBN 10: 3659978205 ISBN 13: 9783659978203
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Da: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germania

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Taschenbuch. Condizione: 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. Codice articolo 9783659978203

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Jiang Lin Qin
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Taschenbuch. Condizione: 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. Codice articolo 9783659978203

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Jiang Lin Qin
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Taschenbuch. Condizione: 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. Codice articolo 9783659978203

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Qin, Jiang Lin
ISBN 10: 3659978205 ISBN 13: 9783659978203
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Paperback. Condizione: Brand New. 84 pages. 8.66x5.91x0.19 inches. In Stock. Codice articolo 3659978205

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