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Class-guided swin transformer for semantic segmentation of remote sensing imagery
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Meng, Xiaoliang, Yang, Yuechi, Wang, Libo, Wang, Teng, Li, Rui and Zhang, Ce (2022) Class-guided swin transformer for semantic segmentation of remote sensing imagery. Geoscience and Remote Sensing Letters, 19 . 6517505. doi:10.1109/LGRS.2022.3215200 ISSN 1545-598X.
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WRAP-class-guided-swin-transformer-semantic-segmentation-remote-sensing-imagery-Li-2022.pdf - Accepted Version - Requires a PDF viewer. Download (3117Kb) | Preview |
Official URL: https://doi.org/10.1109/LGRS.2022.3215200
Abstract
Semantic segmentation of remote sensing images plays a crucial role in a wide variety of practical applications, including land cover mapping, environmental protection, and economic assessment. In the last decade, convolutional neural network (CNN) is the mainstream deep learning based method of semantic segmentation. Compared with conventional methods, CNN-based methods learn semantic features automatically, thereby achieving strong representation capability. However, the local receptive field of the convolution operation limits CNN-based methods from capturing global information. In contrast, Vision Transformer demonstrates its great potential in global information modelling and obtains superior results in semantic segmentation. Inspired by this, in this Letter, we propose a classguided Swin Transformer (CG-Swin) for semantic segmentation of remote sensing images. Specifically, we adopt a Transformerbased encoder-decoder structure, which introduces the Swin Transformer backbone as the encoder and designs a class-guided Transformer block to construct the decoder. The experimental results on ISPRS Vaihingen and Potsdam datasets demonstrate the significant breakthrough of the proposed method over ten benchmarks, outperform both advanced CNN-based and recent Vision Transformers based approaches.
Item Type: | Journal Article | ||||||
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Subjects: | Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software T Technology > TA Engineering (General). Civil engineering (General) T Technology > TK Electrical engineering. Electronics Nuclear engineering |
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Divisions: | Faculty of Science, Engineering and Medicine > Engineering > Engineering | ||||||
Library of Congress Subject Headings (LCSH): | Image segmentation , Remote sensing , Semantic computing, Neural networks (Computer science), Computer vision , Pattern recognition systems | ||||||
Journal or Publication Title: | Geoscience and Remote Sensing Letters | ||||||
Publisher: | Institute of Electrical and Electronics Engineers | ||||||
ISSN: | 1545-598X | ||||||
Official Date: | 17 October 2022 | ||||||
Dates: |
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Volume: | 19 | ||||||
Article Number: | 6517505 | ||||||
DOI: | 10.1109/LGRS.2022.3215200 | ||||||
Status: | Peer Reviewed | ||||||
Publication Status: | Published | ||||||
Reuse Statement (publisher, data, author rights): | © 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. | ||||||
Access rights to Published version: | Restricted or Subscription Access | ||||||
Date of first compliant deposit: | 18 October 2022 | ||||||
Date of first compliant Open Access: | 20 October 2022 | ||||||
RIOXX Funder/Project Grant: |
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