FMAMPN:用于遥感图像语义分割的轻量级特征图注意多径网络

Songqi Hou, Ying Yuan
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引用次数: 0

摘要

深度神经网络在遥感图像语义分割方面表现出色,但现有方法尽管复杂,却往往侧重于相同特征图中的通道和空间依赖关系。这可能导致对不同特征图的统一处理,阻碍信息交流并影响模型功效。为了解决这个问题,我们引入了 "特征图关注"(Feature Map Attention),根据不同特征图之间的相互依赖性动态调节权重。这促进了连接和特征融合,增强了模型表示特征的能力。重要的是,这种改进只需最小的额外计算费用。我们还加入了多路跳转连接,有效地将不同规模的特征从编码器传输到解码器,从而提高了模型的整体效率。我们的轻量级神经网络 FMAMPN 在各种数据集上的表现优于其他最先进的轻量级模型。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
FMAMPN: lightweight feature map attention multipath network for semantic segmentation of remote sensing image
Deep neural networks excel in remote sensing image semantic segmentation, but existing methods, despite their sophistication, often focus on channel and spatial dependencies within identical feature maps. This can lead to a uniform treatment of diverse feature maps, hindering information exchange and impacting model efficacy. To address this, we introduce Feature Map Attention, dynamically modulating weights based on interdependencies among various feature maps. This fosters connections and feature fusion, enhancing the model's capability to represent features. Importantly, this improvement comes with minimal additional computational expense. We also incorporate multipath skip connections, efficiently transmitting features at various scales from encoder to decoder, boosting overall model effectiveness. Our FMAMPN, a lightweight neural network, outperforms other state-of-the-art lightweight models across various datasets.
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