Classification of EgyptSat-1 Images Using Deep Learning Methods

Author(s): Hatem Keshk*, Xu-Cheng Yin.

Journal Name: International Journal of Sensors, Wireless Communications and Control

Volume 10 , Issue 1 , 2020

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Graphical Abstract:


Background: Deep Learning (DL) neural network methods have become a hotspot subject of research in the remote sensing field. Classification of aerial satellite images depends on spectral content, which is a challenging topic in remote sensing.

Objective: With the aim to accomplish a high performance and accuracy of Egyptsat-1 satellite image classification, the use of the Convolutional Neural Network (CNN) is raised in this paper because CNN is considered a leading deep learning method. CNN is developed to classify aerial photographs into land cover classes such as urban, vegetation, desert, water bodies, soil, roads, etc. In our work, a comparison between MAXIMUM Likelihood (ML) which represents the traditional supervised classification methods and CNN method is conducted.

Conclusion: This research finds that CNN outperforms ML by 9%. The convolutional neural network has better classification result, which reached 92.25% as its average accuracy. Also, the experiments showed that the convolutional neural network is the most satisfactory and effective classification method applied to classify Egyptsat-1 satellite images.

Keywords: Classification, convolutional neural network, deep learning, maximum likelihood, satellite image, accuracy.

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Year: 2020
Page: [37 - 46]
Pages: 10
DOI: 10.2174/2210327909666190207153858
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