基于CA-TransUNet的遥感图像道路分割

Remote Sensing Image Road Segmentation Based on CA-TransUNet

针对在背景复杂、地物信息丰富的光学遥感图像中分割道路时存在漏判、误判的问题,提出一种基于CA-TransUNet的遥感图像道路分割方法。以含有多头自注意力的语义分割网络TransUNet为基准,在特征提取模块融入空洞空间金字塔池化,获得不同视野的特征图,通过对各通道信息的整合,增强对多尺度特征的提取;在级联的上采样模块加入混合注意力机制,减少上采样过程细节信息损失,抑制对无关边界信息的注意,并增强道路特征;选择Dice损失函数和二元交叉熵损失联合优化,使光学遥感图像的道路分割更加准确。实验结果表明,提出方法在DeepGlobe数据集上获得的IoU值和F1指数中分别达到56.53%、71.48%,准确率高达97.32%,均高于其他经典遥感图像道路分割算法。在分割周边背景复杂、受障碍物遮挡和细窄道路等情况的遥感图像时,改进的算法能够有效地进行道路分割。

Aiming at the problems of missed and misjudgment of the road segment in optical remote sensing image with complex background and rich feature information,this paper puts forward a method of remote sensing image road segmentation based on CA-TransUNet.The semantic segmentation network TransUNet with multi-head self-attention is taken as the benchmark,and the void space pyramid pooling is integrated into the feature extraction module to obtain the feature maps of different horizons.Through the integration of the information of each channel,the extraction of multi-scale features is enhanced.A hybrid attention mechanism is added to the cascaded upsampling module to reduce the loss of process details,suppress the attention to irrelevant boundary information,and enhance the road features.The Dice loss function and binary cross-entropy loss are selected to opti⁃mize the road segment of optical remote sensing images more accurately.Experimental results show that the proposed method achieves 56.33%IoU value and 71.32%F1 index on DeepGlobe dataset,and the accuracy is up to 97.32%,which is higher than other classical road segmentation algorithms in remote sensing images.The improved algorithm can effectively segment the re⁃mote sensing images with complex surrounding background,obstructed by obstacles and narrow roads.