RS_DCNN: a novel distributed convolutional-neural-networks based-approach for big remote-sensing image classification
Developments in remote sensing technology have led to a continuous increase in the volume of remote-sensing data, which can be qualified as big remote sensing data. A wide range of potential applications is using these data including land cover classification, regional planning, catastrophe predicti...
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Elsevier Ltd.
2021
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my.utm.944162022-03-31T14:54:47Z http://eprints.utm.my/id/eprint/94416/ RS_DCNN: a novel distributed convolutional-neural-networks based-approach for big remote-sensing image classification Boulila, W. Sellami, M. Driss, M. Al-Sarem, M. Safaei, M. Ghaleb, F. A. QA75 Electronic computers. Computer science Developments in remote sensing technology have led to a continuous increase in the volume of remote-sensing data, which can be qualified as big remote sensing data. A wide range of potential applications is using these data including land cover classification, regional planning, catastrophe prediction and management, and climate-change estimation. Big remote sensing data are characterized by different types of resolutions (radiometric, spatial, spectral, and temporal), modes of imaging, and sensor types, and this range of options often makes the process of analyzing and interpreting such data more difficult. In this paper, which is the first study of its kind, we propose a novel distributed deep learning-based approach for the classification of big remote sensing images. Specifically, we propose Distributed Convolutional-Neural-Networks for handling RS image classification (RS-DCNN). The first step is to prepare the training dataset for RS-DCNN. Then, to ensure a data-parallel training on the top of the Apache Spark framework, a pixel-based convolutional-neural-network model across the big data cluster is performed using BigDL. Experiments are conducted on a real dataset covering many regions of Saudi Arabia and the results demonstrate high classification accuracy at a faster speed than other state-of-the-art classification methods. Elsevier Ltd. 2021 Article PeerReviewed Boulila, W. and Sellami, M. and Driss, M. and Al-Sarem, M. and Safaei, M. and Ghaleb, F. A. (2021) RS_DCNN: a novel distributed convolutional-neural-networks based-approach for big remote-sensing image classification. Computers and Electronics in Agriculture, 182 . ISSN 0168-1699 http://dx.doi.org/10.1016/j.compag.2021.106014 DOI: 10.1016/j.compag.2021.106014 |
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QA75 Electronic computers. Computer science Boulila, W. Sellami, M. Driss, M. Al-Sarem, M. Safaei, M. Ghaleb, F. A. RS_DCNN: a novel distributed convolutional-neural-networks based-approach for big remote-sensing image classification |
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Developments in remote sensing technology have led to a continuous increase in the volume of remote-sensing data, which can be qualified as big remote sensing data. A wide range of potential applications is using these data including land cover classification, regional planning, catastrophe prediction and management, and climate-change estimation. Big remote sensing data are characterized by different types of resolutions (radiometric, spatial, spectral, and temporal), modes of imaging, and sensor types, and this range of options often makes the process of analyzing and interpreting such data more difficult. In this paper, which is the first study of its kind, we propose a novel distributed deep learning-based approach for the classification of big remote sensing images. Specifically, we propose Distributed Convolutional-Neural-Networks for handling RS image classification (RS-DCNN). The first step is to prepare the training dataset for RS-DCNN. Then, to ensure a data-parallel training on the top of the Apache Spark framework, a pixel-based convolutional-neural-network model across the big data cluster is performed using BigDL. Experiments are conducted on a real dataset covering many regions of Saudi Arabia and the results demonstrate high classification accuracy at a faster speed than other state-of-the-art classification methods. |
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Article |
author |
Boulila, W. Sellami, M. Driss, M. Al-Sarem, M. Safaei, M. Ghaleb, F. A. |
author_facet |
Boulila, W. Sellami, M. Driss, M. Al-Sarem, M. Safaei, M. Ghaleb, F. A. |
author_sort |
Boulila, W. |
title |
RS_DCNN: a novel distributed convolutional-neural-networks based-approach for big remote-sensing image classification |
title_short |
RS_DCNN: a novel distributed convolutional-neural-networks based-approach for big remote-sensing image classification |
title_full |
RS_DCNN: a novel distributed convolutional-neural-networks based-approach for big remote-sensing image classification |
title_fullStr |
RS_DCNN: a novel distributed convolutional-neural-networks based-approach for big remote-sensing image classification |
title_full_unstemmed |
RS_DCNN: a novel distributed convolutional-neural-networks based-approach for big remote-sensing image classification |
title_sort |
rs_dcnn: a novel distributed convolutional-neural-networks based-approach for big remote-sensing image classification |
publisher |
Elsevier Ltd. |
publishDate |
2021 |
url |
http://eprints.utm.my/id/eprint/94416/ http://dx.doi.org/10.1016/j.compag.2021.106014 |
_version_ |
1729703169235091456 |
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13.149126 |