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

被引:8
|
作者
Jamali, Ali [1 ]
Mahdianpari, Masoud [2 ,3 ]
机构
[1] Univ Karabuk, Fac Engn, Civil Engn Dept, TR-78050 Karabuk, Turkey
[2] Mem Univ Newfoundland, Dept Elect & Comp Engn, St John, NF A1B 3X5, Canada
[3] C CORE, 1 Morrissey Rd, St John, NF A1B 3X5, Canada
关键词
wetland classification; swin transformer; VGG-16; AlexNet; CNN; deep convolutional neural network; New Brunswick; CONVOLUTIONAL NEURAL-NETWORKS; SUPPORT VECTOR MACHINE; RANDOM FOREST; CNN;
D O I
10.3390/w14020178
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
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.
引用
收藏
页数:17
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