针对遥感图像中相似形状地物对道路提取造成干扰的问题，提出基于残差注意力的编-解码网络RAED-Net。RAED-Net的编码网络采用改进的通道注意力残差模块来提取输入图像的局部特征和全局特征，自适应地调整通道特征映射的权重，提高对重要通道信息的关注，减少背景干扰。在解码网络中引入条形卷积模块，提高上采样过程中跨通道信息交互以及对道路边缘细节信息的恢复能力，提升复杂环境中道路提取结果的准确度。在2个不同类型公开数据集上的对比实验结果表明，RAED-Net能够准确提取道路信息，缓解了相似地物对道路提取带来的干扰问题，取得综合最优结果且参数量最少。尤其在全像素标注、复杂性较高的mini DGRD数据集上的F1、IoU和mIoU分别比次优网络提高了3.53%，5.76%和2.21%。


Addressing the interference caused by similar-shaped objects in remote sensing images during road extraction, a residual attention encoder-decoder network (RAED-Net) is proposed. The encoder network of RAED-Net employs an improved channel attention residual module to extract local and global features from the input image. This module adaptively adjusts the weights of channel feature maps, enhancing the focus on important channel information and reducing background interference. In the decoder network, a strip convolution module is introduced to improve cross-channel information interaction during the upsampling process and enhance the ability to recover detailed road edge information, thereby improving the accuracy of road extraction results in complex environments. Comparative experimental results on two different types of public datasets demonstrate that RAED-Net can accurately extract road information, mitigate the interference caused by similar-shaped objects during road extraction, and achieve the best overall results with the smallest number of parameters. Especially on the mini DGRD dataset, which is fully annotated and highly complex, RAED-Net achieves improvements of 3.53%, 5.76%, and 2.21% in F1-score, IoU, and mIoU, respectively, compared to the second-best network.


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