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<p>The core challenge in remote sensing semantic segmentation lies in balancing local detail extraction with global contextual modeling. Existing methods often suffer from limited local receptive fields and inadequate global semantic interaction, leading to the attenuation of small-object features during hierarchical downsampling and fusion. To address these issues, we propose STRNet, a heterogeneous dual-stream encoder–decoder network that integrates a ResNet50 primary encoder and a Swin Transformer auxiliary encoder. This architecture jointly enhances local detail representation and global dependency modeling. Specifically, we design a feature refinement module (FRM) within the ResNet50 encoder to dynamically emphasize task-relevant features via channel attention, improving the recognition of small targets and complex regions. Meanwhile, the swin transformer branch incorporates a detail-preserving compression module (DPCM), which combines deformable convolutions and multi-scale pooling to optimize patch-token downsampling, thereby mitigating spatial detail loss during self-attention encoding. To effectively integrate multi-level features from both branches, we introduce a feature fusion module (FFM) that employs cross-branch channel-attention weighting and multi-scale alignment. The decoder adopts a progressive skip-connection strategy, fusing low-level spatial details with high-level semantics to construct a multi-resolution decoding pyramid for refined segmentation. Extensive experiments conducted on the ISPRS Vaihingen,ISPRS Potsdam, and LoveDA datasets demonstrate that STRNet significantly improves the segmentation accuracy of small objects and achieves competitive performance across all categories, validating its effectiveness for remote sensing semantic segmentation tasks. The code is available at <a href="https://github.com/Z09157/STRNet">https://github.com/Z09157/STRNet</a>.</p>

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STRNet: dual-branch synergistic network with interactive fusion for remote sensing semantic segmentation

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年11月27日

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  • Minjun Zhai,
  • Donghua Chen,
  • Yibo Duan,
  • Qihang Zhen,
  • Jian Zheng,
  • Minmin Pei,
  • Xing Guo

摘要

The core challenge in remote sensing semantic segmentation lies in balancing local detail extraction with global contextual modeling. Existing methods often suffer from limited local receptive fields and inadequate global semantic interaction, leading to the attenuation of small-object features during hierarchical downsampling and fusion. To address these issues, we propose STRNet, a heterogeneous dual-stream encoder–decoder network that integrates a ResNet50 primary encoder and a Swin Transformer auxiliary encoder. This architecture jointly enhances local detail representation and global dependency modeling. Specifically, we design a feature refinement module (FRM) within the ResNet50 encoder to dynamically emphasize task-relevant features via channel attention, improving the recognition of small targets and complex regions. Meanwhile, the swin transformer branch incorporates a detail-preserving compression module (DPCM), which combines deformable convolutions and multi-scale pooling to optimize patch-token downsampling, thereby mitigating spatial detail loss during self-attention encoding. To effectively integrate multi-level features from both branches, we introduce a feature fusion module (FFM) that employs cross-branch channel-attention weighting and multi-scale alignment. The decoder adopts a progressive skip-connection strategy, fusing low-level spatial details with high-level semantics to construct a multi-resolution decoding pyramid for refined segmentation. Extensive experiments conducted on the ISPRS Vaihingen,ISPRS Potsdam, and LoveDA datasets demonstrate that STRNet significantly improves the segmentation accuracy of small objects and achieves competitive performance across all categories, validating its effectiveness for remote sensing semantic segmentation tasks. The code is available at https://github.com/Z09157/STRNet.