Remote Sensing, Nonlinear Model, Supercomputing - Couverture souple

Qin, Jiang Lin

9783659978203: Remote Sensing, Nonlinear Model, Supercomputing

Couverture souple

ISBN 10 : 3659978205 ISBN 13 : 9783659978203

Editeur : LAP LAMBERT Academic Publishing, 2016

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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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� propos de l?auteur

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.

Les informations fournies dans la section � A propos du livre � peuvent faire r�f�rence � une autre �dition de ce titre.

�diteur: LAP LAMBERT Academic Publishing
Date d'�dition: 2016
Langue: anglais
ISBN 10: 3659978205
ISBN 13: 9783659978203
Reliure: Broch�
Nombre de pages: 84
Coordonn�es du fabricant: non disponible
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Remote Sensing, Nonlinear Model, Supercomputing

Jiang Lin Qin

Edit� par LAP LAMBERT Academic Publishing, 2016

ISBN 10 : 3659978205 ISBN 13 : 9783659978203

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Etat : 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. N� de r�f. du vendeur 158964221

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Remote Sensing, Nonlinear Model, Supercomputing

Jiang Lin Qin

Edit� par LAP LAMBERT Academic Publishing, 2016

ISBN 10 : 3659978205 ISBN 13 : 9783659978203

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Taschenbuch. Etat : 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. N� de r�f. du vendeur 9783659978203

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Remote Sensing, Nonlinear Model, Supercomputing

Jiang Lin Qin

Edit� par LAP LAMBERT Academic Publishing Nov 2016, 2016

ISBN 10 : 3659978205 ISBN 13 : 9783659978203

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Taschenbuch. Etat : 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. N� de r�f. du vendeur 9783659978203

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Remote Sensing, Nonlinear Model, Supercomputing

Jiang Lin Qin

Edit� par LAP LAMBERT Academic Publishing Nov 2016, 2016

ISBN 10 : 3659978205 ISBN 13 : 9783659978203

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Vendeur : buchversandmimpf2000, Emtmannsberg, BAYE, Allemagne

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Taschenbuch. Etat : 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, �berseering 33, 22297 Hamburg 84 pp. Englisch. N� de r�f. du vendeur 9783659978203

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