Performance Enhancement of Satellite Image Classification Using a Convolutional Neural Network

Laban N. ; Abdellatif B. ; Ebied H. ; Shedeed H. ; Tolba M. 


Abstract


With dramatically increasing of very resolution of satellite imaging sensors and the daily increasing of remote sensing databases, image classification has been gaining prominence in remote sensing applications. Convolutional neural networks (CNNs) techniques have already been outperforming other classification approaches in various domains. In this paper, we propose an enhance classification of satellite image using CNNs. high information content of satellite images alongside high computational calculations needed by CNNs, that make performance issues very crucial. The enhancement process is based on an efficient selection of adequate image scales that perform respectively, high classification accuracy with least computational burdens. We evaluate the proposed method on three state-of-the-art datasets: UC Merced Land Use Dataset, WHU-RS Dataset and Brazilian Coffee Scenes Dataset. The proposed method leads to a performance enhancement, as opposed to using original scales directly.


Other data

Issue Date 1-Jan-2018
Publisher © 2018, Springer International Publishing AG.
Journal Advances in Intelligent Systems and Computing 
URI http://research.asu.edu.eg/123456789/135
ISBN 9783319648606
DOI http://api.elsevier.com/content/abstract/scopus_id/85029518718
673
639
10.1007/978-3-319-64861-3_63


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