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Article

Swin Transformer for Complex Coastal Wetland Classification Using the Integration of Sentinel-1 and Sentinel-2 Imagery

1
Civil Engineering Department, Faculty of Engineering, University of Karabük, Karabük 78050, Turkey
2
Department of Electrical and Computer Engineering, Memorial University of Newfoundland, St. John’s, NL A1B 3X5, Canada
3
C-CORE, 1 Morrissey Road, St. John’s, NL A1B 3X5, Canada
*
Author to whom correspondence should be addressed.
Academic Editors: Martin Bláha and Kateřina Grabicová
Water 2022, 14(2), 178; https://doi.org/10.3390/w14020178
Received: 8 December 2021 / Revised: 27 December 2021 / Accepted: 6 January 2022 / Published: 10 January 2022
(This article belongs to the Special Issue Freshwater Communities in Human-Altered Ecosystems)
The emergence of deep learning techniques has revolutionized the use of machine learning algorithms to classify complicated environments, notably in remote sensing. Convolutional Neural Networks (CNNs) have shown considerable promise in classifying challenging high-dimensional remote sensing data, particularly in the classification of wetlands. State-of-the-art Natural Language Processing (NLP) algorithms, on the other hand, are transformers. Despite the fact that transformers have been utilized for a few remote sensing applications, they have not been compared to other well-known CNN networks in complex wetland classification. As such, for the classification of complex coastal wetlands in the study area of Saint John city, located in New Brunswick, Canada, we modified and employed the Swin Transformer algorithm. Moreover, the developed transformer classifier results were compared with two well-known deep CNNs of AlexNet and VGG-16. In terms of average accuracy, the proposed Swin Transformer algorithm outperformed the AlexNet and VGG-16 techniques by 14.3% and 44.28%, respectively. The proposed Swin Transformer classifier obtained F-1 scores of 0.65, 0.71, 0.73, 0.78, 0.82, 0.84, and 0.84 for the recognition of coastal marsh, shrub, bog, fen, aquatic bed, forested wetland, and freshwater marsh, respectively. The results achieved in this study suggest the high capability of transformers over very deep CNN networks for the classification of complex landscapes in remote sensing. View Full-Text
Keywords: wetland classification; swin transformer; VGG-16; AlexNet; CNN; deep convolutional neural network; New Brunswick wetland classification; swin transformer; VGG-16; AlexNet; CNN; deep convolutional neural network; New Brunswick
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MDPI and ACS Style

Jamali, A.; Mahdianpari, M. Swin Transformer for Complex Coastal Wetland Classification Using the Integration of Sentinel-1 and Sentinel-2 Imagery. Water 2022, 14, 178. https://doi.org/10.3390/w14020178

AMA Style

Jamali A, Mahdianpari M. Swin Transformer for Complex Coastal Wetland Classification Using the Integration of Sentinel-1 and Sentinel-2 Imagery. Water. 2022; 14(2):178. https://doi.org/10.3390/w14020178

Chicago/Turabian Style

Jamali, Ali, and Masoud Mahdianpari. 2022. "Swin Transformer for Complex Coastal Wetland Classification Using the Integration of Sentinel-1 and Sentinel-2 Imagery" Water 14, no. 2: 178. https://doi.org/10.3390/w14020178

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