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As a huge number of satellites revolve around the earth, a great probability exists to observe and determine the change phenomena on the earth through the analysis of satellite images on a real-time basis. Therefore, classifying satellite images plays strong assistance in remote sensing communities for predicting tropical cyclones. In this article, a classification approach is proposed using Deep Convolutional Neural Network (DCNN), comprising numerous layers, which extract the features through a downsampling process for classifying satellite cloud images. DCNN is trained marvelously on cloud images with an impressive amount of prediction accuracy. Delivery time decreases for testing images, whereas prediction accuracy increases using an appropriate deep convolutional network with a huge number of training dataset instances. The satellite images are taken from the Meteorological & Oceanographic Satellite Data Archival Centre, the organization is responsible for availing satellite cloud images of India and its subcontinent. The proposed cloud image classification shows 94% prediction accuracy with the DCNN framework.


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Deep Convolutional Network Based Machine Intelligence Model for Satellite Cloud Image Classification

Show Author's information Kalyan Kumar Jena1Sourav Kumar Bhoi1Soumya Ranjan Nayak2( )Ranjit Panigrahi3Akash Kumar Bhoi4
Department of Computer Science and Engineering, Parala Maharaja Engineering College, Berhampur 761003, India
Amity School of Engineering and Technology, Amity University, Uttar Pradesh 201303, India
Department of Computer Applications, Sikkim Manipal Institute of Technology, Sikkim Manipal University, Sikkim 737102, India
Directorate of Research, Sikkim Manipal University, Gangtok, Sikkim 737102, India

Abstract

As a huge number of satellites revolve around the earth, a great probability exists to observe and determine the change phenomena on the earth through the analysis of satellite images on a real-time basis. Therefore, classifying satellite images plays strong assistance in remote sensing communities for predicting tropical cyclones. In this article, a classification approach is proposed using Deep Convolutional Neural Network (DCNN), comprising numerous layers, which extract the features through a downsampling process for classifying satellite cloud images. DCNN is trained marvelously on cloud images with an impressive amount of prediction accuracy. Delivery time decreases for testing images, whereas prediction accuracy increases using an appropriate deep convolutional network with a huge number of training dataset instances. The satellite images are taken from the Meteorological & Oceanographic Satellite Data Archival Centre, the organization is responsible for availing satellite cloud images of India and its subcontinent. The proposed cloud image classification shows 94% prediction accuracy with the DCNN framework.

Keywords:

satellite images, satellite image classification, cyclone prediction, Deep Convolutional Neural Network (DCNN), features, layers, down-sampling process
Received: 15 July 2021 Revised: 17 September 2021 Accepted: 18 September 2021 Published: 24 November 2022 Issue date: March 2023
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Publication history

Received: 15 July 2021
Revised: 17 September 2021
Accepted: 18 September 2021
Published: 24 November 2022
Issue date: March 2023

Copyright

© The author(s) 2023.

Acknowledgements

We want to thank Parala Maharaja Engineering College (Govt.), Berhampur, India for providing adequate facility and infrastructure for conducting this major project research work. For this work, we also want to thank Soumya Ranjan Jena, Major Project Group Leader, for supporting this work under our guidance (Kalyan Kumar Jena-Guide and Sourav Kumar Bhoi-Co-Guide).

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The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).

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