Remote Sensing, Nonlinear Model, Supercomputing

Jiang Lin Qin

ISBN 10: 3659978205 / ISBN 13: 9783659978203

Editore: LAP LAMBERT Academic Publishing, 2016

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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

Dati bibliografici

• Titolo: Remote Sensing, Nonlinear Model, Supercomputing
• Editore: LAP LAMBERT Academic Publishing
• Data di pubblicazione: 2016
• Lingua: Inglese
• ISBN 10: 3659978205
• ISBN 13: 9783659978203
• Rilegatura: Taschenbuch
• Condizione: Neu
• Dimensioni: 220 L x 150 A x 6 P (millimetri) 220 millimetri di larghezza per 150 millimetri di altezza per 6 millimetri di profondità
• Peso: 143 grammi

Descrizione articolo

Riassunto

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.

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.