【摘要】 复杂背景下，不同尺度建筑物的特征差异较大，现有算法对多尺度建筑物分割存在分割不均以及误判等问题。为了解决上述问题，本文设计了一种适应多尺度变化的新型网络结构。首先，针对遥感图像场景提分割精度低的问题，引入坐标注意力机制，嵌入到基础网络中增强上下文信息捕获能力，消除噪声的同时增强网络对于空间特征的提取能力。引入了新型递归残差卷积模块，加深网络层次的同时减少信息丢失，提高特征提取效率。最后，在跳跃连接中引入了空洞空间卷积池化金字塔增大网络感受野，增强有效特征，抑制无用特征。设计系统验证模型的实用性。实验结果表明，本文方法在精确率、召回率、F1-score和IoU指标中比U-Net网络分别提高了3.05%、1.56%、1.3%、3.08%。更多还原

【Abstract】 Under complex background, the characteristics of buildings at different scales are quite different, and the existing algorithms have problems such as uneven segmentation and misjudgment of multi-scale building segmentation. To solve the above problems, we design a new network structure adapted to scale changes. Firstly, aiming at the problem of low segmentation accuracy in remote sensing image scenes, we introduce and embed a coordinate attention mechanism in the basic network to enhance the context information capture ability, eliminate noise and enhance the network′s ability to extract spatial features. we introduce A new recursive residual convolution module ed to deepen the network layer, reduce information loss, and improve the efficiency of feature extraction. Finally, we introduce a hollow space convolutional pooled pyramid in the hop connection to increase the network receptive field, enhance the effective features, and suppress the useless features. Design the system to verify the usefulness of the model. Experimental results show that the proposed method improves the accuracy, recall, F1-score, and IoU indicators by 3.05%, 1.56%, 1.3%, and 3.08% compared with the U-Net network, respectively.更多还原