Semantic segmentation for very-high-resolution remote sensing images has been a research hotspot in the field of remote sensing image analysis. However, most existing methods still suffer from a challenge that object boundaries cannot be finely recovered. To tackle the problem, we develop a dual-stream network based on the U-Net architecture, Instead of the traditional skip connections, a boundary attention module is proposed to introduce the boundary information from the EDN module to the SSN module. Experiments on ISPRS Potsdam and Vaihingen datasets show the effectiveness of the proposed network, especially in man-made objects with distinct boundaries.

中文翻译：

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用于 VHR 遥感图像语义分割的边界感知双流网络


极高分辨率遥感图像的语义分割一直是遥感图像分析领域的研究热点。然而，大多数现有方法仍然面临着无法精细恢复对象边界的挑战。为了解决这个问题，我们开发了一个基于U-Net架构的双流网络，代替传统的跳跃连接，提出了边界注意模块来将边界信息从EDN模块引入到SSN模块。在 ISPRS Potsdam 和 Vaihingen 数据集上的实验表明了所提出的网络的有效性，特别是在具有明显边界的人造物体中。