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DBDAN: Dual-Branch Dynamic Attention Network for Semantic Segmentation of Remote Sensing Images

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Pattern Recognition and Computer Vision (PRCV 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14428))

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Abstract

Attention mechanism is capable to capture long-range dependence. However, its independent calculation of correlations can hardly consider the complex background of remote sensing images, which causes noisy and ambiguous attention weights. To address this issue, we design a correlation attention module (CAM) to enhance appropriate correlations and suppress erroneous ones by seeking consensus among all correlation vectors, which facilitates feature aggregation. Simultaneously, we introduce the CAM into a local dynamic attention (LDA) branch and a global dynamic attention (GDA) branch to obtain the information on local texture details and global context, respectively. In addition, considering the different demands of complex and diverse geographical objects for both local texture details and global context, we devise a dynamic weighting mechanism to adaptively adjust the contributions of both branches, thereby constructing a more discriminative feature representation. Experimental results on three datasets suggest that the proposed dual-branch dynamic attention network (DBDAN), which integrates the CAM and both branches, can considerably improve the performance for semantic segmentation of remote sensing images and outperform representative state-of-the-art methods.

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Author information

Authors and Affiliations

  1. School of Software Technology, Zhejiang University, Hangzhou, 310027, China

    Rui Che, Xiaowen Ma, Tingfeng Hong, Xinyu Wang, Tian Feng & Wei Zhang

  2. Innovation Center of Yangtze River Delta, Zhejiang University, Jiaxing, 314103, China

    Wei Zhang

Authors
  1. Rui Che
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  2. Xiaowen Ma
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  3. Tingfeng Hong
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  4. Xinyu Wang
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  5. Tian Feng
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  6. Wei Zhang
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Corresponding author

Correspondence to Wei Zhang .

Editor information

Editors and Affiliations

  1. Nanjing University of Information Science and Technology, Nanjing, China

    Qingshan Liu

  2. Xiamen University, Xiamen, China

    Hanzi Wang

  3. Beijing University of Posts and Telecommunications, Beijing, China

    Zhanyu Ma

  4. Sun Yat-sen University, Guangzhou, China

    Weishi Zheng

  5. Peking University, Beijing, China

    Hongbin Zha

  6. Chinese Academy of Sciences, Beijing, China

    Xilin Chen

  7. Chinese Academy of Sciences, Beijing, China

    Liang Wang

  8. Xiamen University, Xiamen, China

    Rongrong Ji

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© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Che, R., Ma, X., Hong, T., Wang, X., Feng, T., Zhang, W. (2024). DBDAN: Dual-Branch Dynamic Attention Network for Semantic Segmentation of Remote Sensing Images. In: Liu, Q., et al. Pattern Recognition and Computer Vision. PRCV 2023. Lecture Notes in Computer Science, vol 14428. Springer, Singapore. https://doi.org/10.1007/978-981-99-8462-6_25

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  • DOI: https://doi.org/10.1007/978-981-99-8462-6_25

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-99-8461-9

  • Online ISBN: 978-981-99-8462-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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