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    • 摘要: 针对高分辨率遥感影像的道路提取存在道路边缘分割不连续、小目标道路分割精度不高和目标道路误分的问题,本文提出了结合ResNeSt和多尺度特征融合的遥感影像道路提取方法用于遥感影像道路提取 (ResT-UNet)。参考ResNeSt网络模块构造U型网编码器,使前期编码器可以更完整的提取信息,分割目标边缘更加连续;首先在编码器部分引入Triplet Attention注意力机制,抑制无用的特征信息;其次使用卷积块代替最大池化操作,增加特征维度和网络深度,减少道路信息丢失;最后在编码器网络和解码器网络的桥连接部分使用多尺度特征融合模块 (multi-acale feature fusion, MSFF),以捕获区域间的远程依赖关系,提高道路的分割效果。实验在Massachusetts道路数据集和DeepGlobe数据集上进行实验,实验结果表明,该方法分别在数据集上IoU达到了64.76%和64.45%,相比于近几年网络MINet模型提高了1.42%和1.74%,表明ResT-UNet网络有效提高遥感影像道路的提取精度,为解译遥感图像语义信息提供一种新思路。

       

      Abstract: Aiming at the issues of discontinuous road edge segmentation, low accuracy in segmenting small-scale roads, and misclassification of target roads in high-resolution remote sensing imagery, this paper proposes a road extraction method that integrates ResNeSt and multi-scale feature fusion for road extraction from remote sensing imagery. Referencing the ResNeSt network module, a U-shaped network encoder is constructed to enable the initial encoder to extract information more entirely and ensure more continuous segmentation of target edges. Firstly, Triplet Attention is introduced into the encoder to suppress useless feature information. Secondly, convolutional blocks replace max pooling operations, increasing feature dimensionality and network depth while reducing the loss of road information. Finally, a multi-scale feature fusion (MSFF) module is utilized at the bridge connection between the encoder and decoder networks to capture long-range dependencies between regions and improve road segmentation performance. The experiments were conducted on the Massachusetts Roads dataset and the DeepGlobe dataset. The experimental results demonstrate that our proposed method achieved Intersection over Union scores of 65.39% and 65.45% , respectively, on these datasets, representing improvements of 1.42% and 1.74% compared to the original MINet model. These findings indicate that the ResT-UNet network effectively enhances the extraction accuracy of road features in remote sensing imagery, providing a novel approach for interpreting semantic information in remote sensing images.