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Hybrid Shunted Transformer embedding UNet for remote sensing image semantic segmentation

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Abstract

With the development of deep learning, Remote Sensing Image (RSI) semantic segmentation has produced significant advances. However, due to the sparse distribution of the objects and the high similarity between classes, the task of semantic segmentation in RSI is still extremely challenging. In this paper, we propose a novel semantic segmentation framework for RSI called HST-UNet that can overcome the shortcomings of the existing models and extract and recover the global and local features of RSI, which is a hybrid semantic segmentation model with Shunted Transformer as encoder and Multi-Scale Convolutional Attention Network (MSCAN) as decoder. Then, to better fuse the information from the Encoder and the Decoder and alleviate the ambiguity, we design a Learnable Weighted Fusion (LWF) module to effectively connect to the decoder features. Extensive experiments demonstrate that the proposed HST-UNet outperforms the state-of-the-art methods, achieving F1 score/MIoU accuracy of 71.44%/83.00% on the ISPRS Vaihingen dataset and 77.36%/87.09% on ISPRS Potsdam dataset. The code will be available at https://github.com/HC-Zhou/HST-UNet.

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Data availability

The data that support the findings of this study are available at http://www2.isprs.org/commissions/comm3/wg4/semantic-labeling.html with corresponding permission.

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Acknowledgments

This study was supported by National Natural Science Foundation of China (Grant Nos. 62006049, 62172113, and 62072123), Guangdong Basic and Applied Basic Research Foundation (No. 2023A1515010939), Project of Education Department of Guangdong Province (Grant Nos. 2022KTSCX068 and 2020ZDZX3059), The Ministry of education of Humanities and Social Science project (Grant No. 18JDGC012), Guangdong Science and Technology Project (Grant Nos. KTP20210197 and 2017A040403068), and Guangdong Science and Technology Innovation Strategy Special Fund Project (Climbing Plan) (No. pdjh2022b0302).

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Author notes

  1. Huacong Zhou and Xiangling Xiao have contributed equally to this work.

Authors and Affiliations

  1. School of Computer Science, Guangdong Polytechnic Normal University, Guangzhou, 510630, Guangdong, China

    Huacong Zhou, Xiangling Xiao, Huihui Li & Peng Liang

  2. Guangdong Provincial Key Laboratory of Intellectual Property and Big Data, Guangdong Polytechnic Normal University, Guangzhou, 510665, Guangdong, China

    Huihui Li

  3. School of Data Science and Engineering, Guangdong Polytechnic Normal University, Guangzhou, 510630, Guangdong, China

    Xiaoyong Liu

  4. Academy of Heyuan, Guangdong Polytechnic Normal University, Heyuan, 517099, Guangdong, China

    Xiaoyong Liu

  5. The Cyberspace Institute of Advanced Technology, Guangzhou University, Guangdong, 510006, Guangzhou, China

    Huacong Zhou

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  1. Huacong Zhou
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Contributions

Huacong Zhou performed conceptualization, software, and writing—original draft; Xiangling Xiao performed validation, formal analysis, and writing—review and editing; Huihui Li performed methodology and funding acquisition; Xiaoyong Liu performed supervision and funding acquisition; and Peng Liang performed resources, validation, investigation, and funding acquisition.

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Correspondence to Huihui Li or Xiaoyong Liu.

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Zhou, H., Xiao, X., Li, H. et al. Hybrid Shunted Transformer embedding UNet for remote sensing image semantic segmentation. Neural Comput & Applic 36, 15705–15720 (2024). https://doi.org/10.1007/s00521-024-09888-4

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