Comparing Solo Versus Ensemble Convolutional Neural Networks for Wetland Classification Using Multi-Spectral Satellite Imagery
Wetlands are important ecosystems that are linked to climate change mitigation. As 25% of global wetlands are located in Canada, accurate and up-to-date wetland classification is of high importance, nationally and internationally. The advent of deep learning techniques has revolutionized the current...
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ftmdpi:oai:mdpi.com:/2072-4292/13/11/2046/ 2023-08-20T04:08:05+02:00 Comparing Solo Versus Ensemble Convolutional Neural Networks for Wetland Classification Using Multi-Spectral Satellite Imagery Ali Jamali Masoud Mahdianpari Brian Brisco Jean Granger Fariba Mohammadimanesh Bahram Salehi agris 2021-05-22 application/pdf https://doi.org/10.3390/rs13112046 EN eng Multidisciplinary Digital Publishing Institute Forest Remote Sensing https://dx.doi.org/10.3390/rs13112046 https://creativecommons.org/licenses/by/4.0/ Remote Sensing; Volume 13; Issue 11; Pages: 2046 deep learning wetland mapping convolutional neural network satellite image classification ensemble learning Text 2021 ftmdpi https://doi.org/10.3390/rs13112046 2023-08-01T01:46:50Z Wetlands are important ecosystems that are linked to climate change mitigation. As 25% of global wetlands are located in Canada, accurate and up-to-date wetland classification is of high importance, nationally and internationally. The advent of deep learning techniques has revolutionized the current use of machine learning algorithms to classify complex environments, specifically in remote sensing. In this paper, we explore the potential and possible limitations to be overcome regarding the use of ensemble deep learning techniques for complex wetland classification and discusses the potential and limitation of various solo convolutional neural networks (CNNs), including DenseNet, GoogLeNet, ShuffleNet, MobileNet, Xception, Inception-ResNet, ResNet18, and ResNet101 in three different study areas located in Newfoundland and Labrador, Canada (i.e., Avalon, Gros Morne, and Grand Falls). Moreover, to improve the classification accuracies of wetland classes of bog, fen, marsh, swamp, and shallow water, the results of the three best CNNs in each study area is fused using three supervised classifiers of random forest (RF), bagged tree (BTree), Bayesian optimized tree (BOT), and one unsupervised majority voting classifier. The results suggest that the ensemble models, in particular BTree, have a valuable role to play in the classification of wetland classes of bog, fen, marsh, swamp, and shallow water. The ensemble CNNs show an improvement of 9.63–19.04% in terms of mean producer’s accuracy compared to the solo CNNs, to recognize wetland classes in three different study areas. This research indicates a promising potential for integrating ensemble-based learning and deep learning for operational large area land cover, particularly complex wetland type classification. Text Newfoundland MDPI Open Access Publishing Newfoundland Canada Remote Sensing 13 11 2046 |
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Open Polar |
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MDPI Open Access Publishing |
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language |
English |
topic |
deep learning wetland mapping convolutional neural network satellite image classification ensemble learning |
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deep learning wetland mapping convolutional neural network satellite image classification ensemble learning Ali Jamali Masoud Mahdianpari Brian Brisco Jean Granger Fariba Mohammadimanesh Bahram Salehi Comparing Solo Versus Ensemble Convolutional Neural Networks for Wetland Classification Using Multi-Spectral Satellite Imagery |
topic_facet |
deep learning wetland mapping convolutional neural network satellite image classification ensemble learning |
description |
Wetlands are important ecosystems that are linked to climate change mitigation. As 25% of global wetlands are located in Canada, accurate and up-to-date wetland classification is of high importance, nationally and internationally. The advent of deep learning techniques has revolutionized the current use of machine learning algorithms to classify complex environments, specifically in remote sensing. In this paper, we explore the potential and possible limitations to be overcome regarding the use of ensemble deep learning techniques for complex wetland classification and discusses the potential and limitation of various solo convolutional neural networks (CNNs), including DenseNet, GoogLeNet, ShuffleNet, MobileNet, Xception, Inception-ResNet, ResNet18, and ResNet101 in three different study areas located in Newfoundland and Labrador, Canada (i.e., Avalon, Gros Morne, and Grand Falls). Moreover, to improve the classification accuracies of wetland classes of bog, fen, marsh, swamp, and shallow water, the results of the three best CNNs in each study area is fused using three supervised classifiers of random forest (RF), bagged tree (BTree), Bayesian optimized tree (BOT), and one unsupervised majority voting classifier. The results suggest that the ensemble models, in particular BTree, have a valuable role to play in the classification of wetland classes of bog, fen, marsh, swamp, and shallow water. The ensemble CNNs show an improvement of 9.63–19.04% in terms of mean producer’s accuracy compared to the solo CNNs, to recognize wetland classes in three different study areas. This research indicates a promising potential for integrating ensemble-based learning and deep learning for operational large area land cover, particularly complex wetland type classification. |
format |
Text |
author |
Ali Jamali Masoud Mahdianpari Brian Brisco Jean Granger Fariba Mohammadimanesh Bahram Salehi |
author_facet |
Ali Jamali Masoud Mahdianpari Brian Brisco Jean Granger Fariba Mohammadimanesh Bahram Salehi |
author_sort |
Ali Jamali |
title |
Comparing Solo Versus Ensemble Convolutional Neural Networks for Wetland Classification Using Multi-Spectral Satellite Imagery |
title_short |
Comparing Solo Versus Ensemble Convolutional Neural Networks for Wetland Classification Using Multi-Spectral Satellite Imagery |
title_full |
Comparing Solo Versus Ensemble Convolutional Neural Networks for Wetland Classification Using Multi-Spectral Satellite Imagery |
title_fullStr |
Comparing Solo Versus Ensemble Convolutional Neural Networks for Wetland Classification Using Multi-Spectral Satellite Imagery |
title_full_unstemmed |
Comparing Solo Versus Ensemble Convolutional Neural Networks for Wetland Classification Using Multi-Spectral Satellite Imagery |
title_sort |
comparing solo versus ensemble convolutional neural networks for wetland classification using multi-spectral satellite imagery |
publisher |
Multidisciplinary Digital Publishing Institute |
publishDate |
2021 |
url |
https://doi.org/10.3390/rs13112046 |
op_coverage |
agris |
geographic |
Newfoundland Canada |
geographic_facet |
Newfoundland Canada |
genre |
Newfoundland |
genre_facet |
Newfoundland |
op_source |
Remote Sensing; Volume 13; Issue 11; Pages: 2046 |
op_relation |
Forest Remote Sensing https://dx.doi.org/10.3390/rs13112046 |
op_rights |
https://creativecommons.org/licenses/by/4.0/ |
op_doi |
https://doi.org/10.3390/rs13112046 |
container_title |
Remote Sensing |
container_volume |
13 |
container_issue |
11 |
container_start_page |
2046 |
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