Class-guided swin transformer for semantic segmentation of remote sensing imagery

Meng, Xiaoliang; Yang, Yuechi; Wang, Libo; Wang, Teng; Li, Rui; Zhang, Ce ORCID: https://orcid.org/0000-0001-5100-3584. 2022 Class-guided swin transformer for semantic segmentation of remote sensing imagery. IEEE Geoscience and Remote Sensing Letters, 19, 6517505. 5, pp. 10.1109/LGRS.2022.3215200

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Abstract/Summary

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 long-range dependencies. In contrast, Vision Transformer (ViT) demonstrates its great potential in modeling long-range dependencies and obtains superior results in semantic segmentation. Inspired by this, in this letter, we propose a class-guided Swin Transformer (CG-Swin) for semantic segmentation of remote sensing images. Specifically, we adopt a Transformer-based 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, outperforming both advanced CNN-based and recent Transformer-based approaches.

Item Type:	Publication - Article
Digital Object Identifier (DOI):	10.1109/LGRS.2022.3215200
UKCEH and CEH Sections/Science Areas:	Soils and Land Use (Science Area 2017-)
ISSN:	1545-598X
Additional Information. Not used in RCUK Gateway to Research.:	Full text of accepted version available via Related URLs link.
Additional Keywords:	fully transformer network, class-guided mechanism, semantic segmentation, remote sensing
NORA Subject Terms:	Electronics, Engineering and Technology
Related URLs:	Other
Date made live:	01 Feb 2024 11:46 +0 (UTC)
URI:	https://nora.nerc.ac.uk/id/eprint/536827

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