{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,8]],"date-time":"2026-04-08T18:47:42Z","timestamp":1775674062170,"version":"3.50.1"},"reference-count":62,"publisher":"MDPI AG","issue":"14","license":[{"start":{"date-parts":[[2024,7,22]],"date-time":"2024-07-22T00:00:00Z","timestamp":1721606400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Natural Sciences and Engineering Research Council of Canada (NSERC) Discovery Grants program","award":["RGPIN-2022-04766"],"award-info":[{"award-number":["RGPIN-2022-04766"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Accurate and efficient classification of wetlands, as one of the most valuable ecological resources, using satellite remote sensing data is essential for effective environmental monitoring and sustainable land management. Deep learning models have recently shown significant promise for identifying wetland land cover; however, they are mostly constrained in practical issues regarding efficiency while gaining high accuracy with limited training ground truth samples. To address these limitations, in this study, a novel deep learning model, namely Wet-ConViT, is designed for the precise mapping of wetlands using multi-source satellite data, combining the strengths of multispectral Sentinel-2 and SAR Sentinel-1 datasets. Both capturing local information of convolution and the long-range feature extraction capabilities of transformers are considered within the proposed architecture. Specifically, the key to Wet-ConViT\u2019s foundation is the multi-head convolutional attention (MHCA) module that integrates convolutional operations into a transformer attention mechanism. By leveraging convolutions, MHCA optimizes the efficiency of the original transformer self-attention mechanism. This resulted in high-precision land cover classification accuracy with a minimal computational complexity compared with other state-of-the-art models, including two convolutional neural networks (CNNs), two transformers, and two hybrid CNN\u2013transformer models. In particular, Wet-ConViT demonstrated superior performance for classifying land cover with approximately 95% overall accuracy metrics, excelling the next best model, hybrid CoAtNet, by about 2%. The results highlighted the proposed architecture\u2019s high precision and efficiency in terms of parameters, memory usage, and processing time. Wet-ConViT could be useful for practical wetland mapping tasks, where precision and computational efficiency are paramount.<\/jats:p>","DOI":"10.3390\/rs16142673","type":"journal-article","created":{"date-parts":[[2024,7,22]],"date-time":"2024-07-22T12:20:38Z","timestamp":1721650838000},"page":"2673","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Wet-ConViT: A Hybrid Convolutional\u2013Transformer Model for Efficient Wetland Classification Using Satellite Data"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7030-8794","authenticated-orcid":false,"given":"Ali","family":"Radman","sequence":"first","affiliation":[{"name":"Department of Electrical and Computer Engineering, Memorial University of Newfoundland, St. John\u2019s, NL A1B 3X5, Canada"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9472-2324","authenticated-orcid":false,"given":"Fariba","family":"Mohammadimanesh","sequence":"additional","affiliation":[{"name":"Canada Centre for Remote Sensing, Natural Resources Canada, 580 Booth Street, Ottawa, ON K1A 1M1, Canada"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7234-959X","authenticated-orcid":false,"given":"Masoud","family":"Mahdianpari","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, Memorial University of Newfoundland, St. John\u2019s, NL A1B 3X5, Canada"},{"name":"C-CORE, St. John\u2019s, NL A1B 3X5, Canada"}]}],"member":"1968","published-online":{"date-parts":[[2024,7,22]]},"reference":[{"key":"ref_1","first-page":"103095","article-title":"A Deep Learning Framework Based on Generative Adversarial Networks and Vision Transformer for Complex Wetland Classification Using Limited Training Samples","volume":"115","author":"Jamali","year":"2022","journal-title":"Int. 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