计算机科学 ›› 2025, Vol. 52 ›› Issue (5): 212-219.doi: 10.11896/jsjkx.240300137
姜文文, 夏英
JIANG Wenwen, XIA Ying
摘要： 遥感图像的空间分辨率高,不同类型对象的尺度差异大、类别不平衡,是精准语义分割任务所面临的主要挑战。为了提高遥感图像语义分割的准确性,提出了一种改进U-Net的多尺度特征融合遥感图像语义分割网络(Multi-scale Feature Fusion Network,MFFNet)。该网络以U-Net网络为基础,包含动态特征融合模块和门控注意力卷积混合模块。其中,动态特征融合模块代替跳跃连接,改进上采样层和下采样层的特征融合方式,避免特征融合导致信息丢失,同时提高浅层特征和深层特征的融合效果;门控注意力卷积混合模块通过整合自注意力、卷积和门控机制,更好地捕获局部和全局信息。在Potsdam和Vaihingen数据集上开展对比实验和消融实验,结果表明MFFNet在两个数据集上的mIoU分别达到76.95%和72.93%,有效提高了遥感图像的语义分割精度。
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