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Classification of EgyptSat-1 Images Using Deep Learning Methods


Hatem Keshk* and Xu-Cheng Yin   Pages 1 - 10 ( 10 )


Background & Objective: 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. 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.


Deep Learning, Classification, Satellite image, Convolutional Neural Network


Data Reception, Analysis, receiving and station affairs, National Authority for Remote Sensing and Space Science, Cairo, Pattern Recognition and Image Processing, School of Engineering and Computer Science, University of Science and Technology Beijing, Beijing

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