Deep Convolutional Network Based Machine Intelligence Model for Satellite Cloud Image Classification
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...
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| Format: | Article |
| Language: | English |
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Tsinghua University Press
2023-03-01
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| Series: | Big Data Mining and Analytics |
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| Online Access: | https://www.sciopen.com/article/10.26599/BDMA.2021.9020017 |
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| author | Kalyan Kumar Jena Sourav Kumar Bhoi Soumya Ranjan Nayak Ranjit Panigrahi Akash Kumar Bhoi |
| author_facet | Kalyan Kumar Jena Sourav Kumar Bhoi Soumya Ranjan Nayak Ranjit Panigrahi Akash Kumar Bhoi |
| author_sort | Kalyan Kumar Jena |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-0e60497bbeda47dbae8b9797f28efbc9 |
| institution | Kabale University |
| issn | 2096-0654 |
| language | English |
| publishDate | 2023-03-01 |
| publisher | Tsinghua University Press |
| record_format | Article |
| series | Big Data Mining and Analytics |
| spelling | doaj-art-0e60497bbeda47dbae8b9797f28efbc92025-02-03T03:00:39ZengTsinghua University PressBig Data Mining and Analytics2096-06542023-03-0161324310.26599/BDMA.2021.9020017Deep Convolutional Network Based Machine Intelligence Model for Satellite Cloud Image ClassificationKalyan Kumar Jena0Sourav Kumar Bhoi1Soumya Ranjan Nayak2Ranjit Panigrahi3Akash Kumar Bhoi4Department of Computer Science and Engineering, Parala Maharaja Engineering College, Berhampur 761003, IndiaDepartment of Computer Science and Engineering, Parala Maharaja Engineering College, Berhampur 761003, IndiaAmity School of Engineering and Technology, Amity University, Uttar Pradesh 201303, IndiaDepartment of Computer Applications, Sikkim Manipal Institute of Technology, Sikkim Manipal University, Sikkim 737102, IndiaDirectorate of Research, Sikkim Manipal University, Gangtok, Sikkim 737102, IndiaAs 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.https://www.sciopen.com/article/10.26599/BDMA.2021.9020017satellite imagessatellite image classificationcyclone predictiondeep convolutional neural network (dcnn)featureslayersdown-sampling process |
| spellingShingle | Kalyan Kumar Jena Sourav Kumar Bhoi Soumya Ranjan Nayak Ranjit Panigrahi Akash Kumar Bhoi Deep Convolutional Network Based Machine Intelligence Model for Satellite Cloud Image Classification Big Data Mining and Analytics satellite images satellite image classification cyclone prediction deep convolutional neural network (dcnn) features layers down-sampling process |
| title | Deep Convolutional Network Based Machine Intelligence Model for Satellite Cloud Image Classification |
| title_full | Deep Convolutional Network Based Machine Intelligence Model for Satellite Cloud Image Classification |
| title_fullStr | Deep Convolutional Network Based Machine Intelligence Model for Satellite Cloud Image Classification |
| title_full_unstemmed | Deep Convolutional Network Based Machine Intelligence Model for Satellite Cloud Image Classification |
| title_short | Deep Convolutional Network Based Machine Intelligence Model for Satellite Cloud Image Classification |
| title_sort | deep convolutional network based machine intelligence model for satellite cloud image classification |
| topic | satellite images satellite image classification cyclone prediction deep convolutional neural network (dcnn) features layers down-sampling process |
| url | https://www.sciopen.com/article/10.26599/BDMA.2021.9020017 |
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