Class-Guided Swin Transformer for Semantic Segmentation of Remote Sensing Imagery

Xiaoliang Meng, Yuechi Yang, Libo Wang*, Teng Wang, Rui Li, Ce Zhang

*Corresponding author for this work

Research output: Contribution to journalArticle (Academic Journal)peer-review

27 Citations (Scopus)


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.

Original languageEnglish
Article number6517505
Pages (from-to)1-5
Number of pages5
JournalIEEE Geoscience and Remote Sensing Letters
Publication statusPublished - 17 Oct 2022

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  • Class-guided mechanism
  • fully transformer network
  • remote sensing
  • semantic segmentation


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