DGCFNet:双全球上下文融合网络用于遥感图像语义细分
Yuan Liao1, Tongchi Zhou2, Lu Li3
1School of Information and Communication Engineering, Zhongyuan University of Technology, Zhengzhou, China.
PeerJ. Computer science
|June 26, 2025
概括
本研究介绍了用于高精度遥感图像语义细分的双全球上下文融合网络 (DGCFNet). DGCFNet有效地结合了卷积神经网络和变压器,以捕获本地和全球的上下文信息,以提高准确性.
科学领域:
- 计算机视觉 计算机视觉
- 遥感 遥感 遥感 遥感
- 人工智能的人工智能
背景情况:
- 遥感图像的语义细分由于复杂的背景和特征变化而具有挑战性.
- 卷积神经网络 (CNN) 在局部特征提取方面表现出色,但在远程依赖方面却很难.
- 变压器通过自我注意力有效地捕捉全球背景,但可能会忽略当地的细节.
研究的目的:
- 提出一个新的语义细分网络,双全球上下文融合网络 (DGCFNet),用于高精度遥感图像分析.
- 整合CNN的优势,以提供本地信息,以及全球背景的变压器.
- 通过解决诸如高跨类相似性和类内变异等挑战来提高细分性能.
主要方法:
- 开发了DGCFNet,这是一个结合CNN和变压器架构的编码解码器网络.
- 引入了一个双分支的全球提取模块,以增强变压器的全球上下文建模,同时保留本地信息.
- 整合了跨层次信息交互模块和功能交互指导模块,以改善功能相关性和细分一致性.
主要成果:
- 在Vaihingen,Potsdam和BLU数据集上,DGCFNet实现了卓越的细分性能.
- 拟议的方法在各自的数据集上获得了82.20%,83.84%和68.87%的欧盟交叉点 (mIoU) 平均得分.
- 实验结果证明了综合局部和全球背景建模方法的有效性.
结论:
- 拟议的DGCFNet有效地解决了遥感图像语义细分方面的挑战.
- 该网络能够融合本地和全球上下文信息,从而带来显著的性能改善.
- 在遥感应用中,DGCFNet为准确和强大的语义细分提供了一个有希望的方法.
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