Remote Sensing, Nonlinear Model, Supercomputing

Jiang Lin Qin

ISBN 10: 3659978205 / ISBN 13: 9783659978203

Published by LAP LAMBERT Academic Publishing Nov 2016, 2016

About this Item

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.

Seller Inventory # 9783659978203

Bibliographic Details

• Title: Remote Sensing, Nonlinear Model, Supercomputing
• Publisher: LAP LAMBERT Academic Publishing Nov 2016
• Publication Date: 2016
• Language: English
• ISBN 10: 3659978205
• ISBN 13: 9783659978203
• Binding: Taschenbuch
• Condition: Neu

About this title

Synopsis

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.

About the Author

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.