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Article

A Synergic Use of Sentinel-1 and Sentinel-2 Imagery for Complex Wetland Classification Using Generative Adversarial Network (GAN) Scheme

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Civil Engineering Department, Faculty of Engineering, University of Karabük, Karabük 78050, Turkey
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Department of Electrical and Computer Engineering, Memorial University of Newfoundland, St. John’s, NL A1B 3X5, Canada
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C-CORE, 1 Morrissey Road, St. John’s, NL A1B 3X5, Canada
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The Canada Centre for Mapping and Earth Observation, Ottawa, ON K1S 5K2, Canada
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Department of Environmental Resources Engineering, College of Environmental Science and Forestry (SUNY ESF), State University of New York, Syracuse, NY 13210, USA
*
Author to whom correspondence should be addressed.
Academic Editor: Chang Huang
Water 2021, 13(24), 3601; https://doi.org/10.3390/w13243601
Received: 1 November 2021 / Revised: 30 November 2021 / Accepted: 13 December 2021 / Published: 15 December 2021
(This article belongs to the Special Issue Mapping and Monitoring of Wetlands)
Due to anthropogenic activities and climate change, many natural ecosystems, especially wetlands, are lost or changing at a rapid pace. For the last decade, there has been increasing attention towards developing new tools and methods for the mapping and classification of wetlands using remote sensing. At the same time, advances in artificial intelligence and machine learning, particularly deep learning models, have provided opportunities to advance wetland classification methods. However, the developed deep and very deep algorithms require a higher number of training samples, which is costly, logistically demanding, and time-consuming. As such, in this study, we propose a Deep Convolutional Neural Network (DCNN) that uses a modified architecture of the well-known DCNN of the AlexNet and a Generative Adversarial Network (GAN) for the generation and classification of Sentinel-1 and Sentinel-2 data. Applying to an area of approximately 370 sq. km in the Avalon Peninsula, Newfoundland, the proposed model with an average accuracy of 92.30% resulted in F-1 scores of 0.82, 0.85, 0.87, 0.89, and 0.95 for the recognition of swamp, fen, marsh, bog, and shallow water, respectively. Moreover, the proposed DCNN model improved the F-1 score of bog, marsh, fen, and swamp wetland classes by 4%, 8%, 11%, and 26%, respectively, compared to the original CNN network of AlexNet. These results reveal that the proposed model is highly capable of the generation and classification of Sentinel-1 and Sentinel-2 wetland samples and can be used for large-extent classification problems. View Full-Text
Keywords: wetland classification; machine learning; CNN; Deep Convolutional Neural Network; Generative Adversarial Network wetland classification; machine learning; CNN; Deep Convolutional Neural Network; Generative Adversarial Network
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MDPI and ACS Style

Jamali, A.; Mahdianpari, M.; Mohammadimanesh, F.; Brisco, B.; Salehi, B. A Synergic Use of Sentinel-1 and Sentinel-2 Imagery for Complex Wetland Classification Using Generative Adversarial Network (GAN) Scheme. Water 2021, 13, 3601. https://doi.org/10.3390/w13243601

AMA Style

Jamali A, Mahdianpari M, Mohammadimanesh F, Brisco B, Salehi B. A Synergic Use of Sentinel-1 and Sentinel-2 Imagery for Complex Wetland Classification Using Generative Adversarial Network (GAN) Scheme. Water. 2021; 13(24):3601. https://doi.org/10.3390/w13243601

Chicago/Turabian Style

Jamali, Ali, Masoud Mahdianpari, Fariba Mohammadimanesh, Brian Brisco, and Bahram Salehi. 2021. "A Synergic Use of Sentinel-1 and Sentinel-2 Imagery for Complex Wetland Classification Using Generative Adversarial Network (GAN) Scheme" Water 13, no. 24: 3601. https://doi.org/10.3390/w13243601

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