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Open Access

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

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
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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 imagessatellite image classificationcyclone predictionDeep Convolutional Neural Network (DCNN)featureslayersdown-sampling process

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Big Data Mining and Analytics
Volume 6 Issue 1,
March 2023
Pages 32-43
DOI: 10.26599/BDMA.2021.9020017
Cite this article:
Jena KK, Bhoi SK, Nayak SR, et al. Deep Convolutional Network Based Machine Intelligence Model for Satellite Cloud Image Classification. Big Data Mining and Analytics, 2023, 6(1): 32-43. https://doi.org/10.26599/BDMA.2021.9020017

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Received: 15 July 2021
Revised: 17 September 2021
Accepted: 18 September 2021
Published: 24 November 2022
© The author(s) 2023.

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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