Volume 15 Number 2 (Mar. 2020)
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JCP 2020 Vol.15(2): 48-58 ISSN: 1796-203X
doi: 10.17706/jcp.15.2.48-58

An Efficient Convolutional Neural Network for Remote-Sensing Scene Image Classification

Muhammad Ashad Baloch1, Sajid Ali2, Mubashir H.Malik3, Aamir Hussain4, Abdul Mustaan Madni1
1National College of Business Administration & Economics Multan, Pakistan.
2Department of Information Science, University of Education Lahore, Pakistan.
3Institute of Southern Punjab Multan, Pakistan.
4Muhammad Nawaz Shareef University of Agriculture Multan, Pakistan.
….

Abstract—Deep neural networks are providing a powerful solution for remote-sensing scene image classification. However, a limited number of training samples, inter-class similarity among scene categories, and to get the benefits of multi-layer features remains a significant challenge in the remote sensing domain. Many efforts have been proposed to deal the above challenges by adapting knowledge of state-of-the-art networks such as AlexNet, GoogleNet, OverFeat, etc. However, these networks have high number of parameters. This research proposes a five-layer architecture which has fewer parameters compared with above state-of-the-art networks, and can be also complementary to other convolutional neural network features. Extensive experiments on UC Merced and WHU-RS datasets prove that although our network decreases the number of parameters dramatically, it generates more accurate results than AlexNet, OverFeat, and its accuracy is comparable with other state-of-the-art methods.

Index Terms—Satellite image classification, convolutional neural network, feature fusion.

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Cite: Muhammad Ashad Baloch, Sajid Ali, Mubashir H.Malik, Aamir Hussain, Abdul Mustaan Madni, "An Efficient Convolutional Neural Network for Remote-Sensing Scene Image Classification" Journal of Computers vol. 15, no. 2, pp. 48-58 , 2020.
Copyright © 2020 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0). 

General Information

ISSN: 1796-203X
Abbreviated Title: J.Comput.
Frequency: Bimonthly
Editor-in-Chief: Prof. Liansheng Tan
Executive Editor: Ms. Nina Lee
Abstracting/ Indexing: DBLP, EBSCO,  ProQuest, INSPEC, ULRICH's Periodicals Directory, WorldCat, CNKI,etc
E-mail: jcp@iap.org
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