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<p>Semantic segmentation of remotely sensed images is crucial for urban planning and change detection, yet faces issues like sample imbalance and low data quality. This study compiles a GF-2 image dataset and refines the PSPNet model. Weights of different class samples were adjusted to prioritize minority classes, mitigating sample imbalance’s impact on classification. Data augmentation enhanced dataset quality. By replacing ResNet with the STFF network for better global feature extraction, adding attention modules, and using a combined loss function, the improved model shows excellent performance. It achieves a mAcc of 90.32 <InlineEquation ID="IEq1"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="41598_2025_89094_Article_IEq1.gif" Format="GIF" Height="16" Rendition="HTML" Resolution="72" Type="Linedraw" Width="15" /> </InlineMediaObject> <EquationSource Format="TEX">\(\%\)</EquationSource> </InlineEquation>, mIoU of 76.04 <InlineEquation ID="IEq2"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="41598_2025_89094_Article_IEq1.gif" Format="GIF" Height="16" Rendition="HTML" Resolution="72" Type="Linedraw" Width="15" /> </InlineMediaObject> <EquationSource Format="TEX">\(\%\)</EquationSource> </InlineEquation>, and a Dice coefficient of 85.15 <InlineEquation ID="IEq3"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="41598_2025_89094_Article_IEq1.gif" Format="GIF" Height="16" Rendition="HTML" Resolution="72" Type="Linedraw" Width="15" /> </InlineMediaObject> <EquationSource Format="TEX">\(\%\)</EquationSource> </InlineEquation>. Comparison with other models verifies its superiority, and tests on public datasets prove strong generalization, offering valuable insights for remote sensing image processing.</p>

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Enhanced remote sensing image feature classification using STFF-PSPNet

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年7月29日

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  • Haiying Li,
  • Jiaqi Gao,
  • Yang Liu,
  • Chenxi Huang,
  • Lijun Li,
  • Liqiang Zhang

摘要

Semantic segmentation of remotely sensed images is crucial for urban planning and change detection, yet faces issues like sample imbalance and low data quality. This study compiles a GF-2 image dataset and refines the PSPNet model. Weights of different class samples were adjusted to prioritize minority classes, mitigating sample imbalance’s impact on classification. Data augmentation enhanced dataset quality. By replacing ResNet with the STFF network for better global feature extraction, adding attention modules, and using a combined loss function, the improved model shows excellent performance. It achieves a mAcc of 90.32 \(\%\) , mIoU of 76.04 \(\%\) , and a Dice coefficient of 85.15 \(\%\) . Comparison with other models verifies its superiority, and tests on public datasets prove strong generalization, offering valuable insights for remote sensing image processing.