一种改进U-Net的高分辨率遥感影像道路提取方法

从遥感影像中准确高效地提取道路信息，对基础地理数据库的建立与维护具有重大意义。高分辨率遥感影像背景信息复杂，导致现有算法无法较好地从中提取道路信息。U-Net网络在图像分割方面有较好的实验效果，但道路分割结果准确性不佳，因此，提出了一种改进U-Net网络的高分辨率影像道路提取方法。首先，设计基于U-Net的网络结构，将VGG16作为网络编码结构，可更好地提取特征语义信息；其次，利用Batch Normalization与Dropout解决网络训练过程中出现的过拟合；最后，对训练数据利用旋转与镜像变换进行扩充，采用ELU激活函数，提升了网络训练速度。实验结果表明：该方法可以较为准确高效地提取道路信息。

High-resolution Remote Sensing Image Road Extraction Method for Improving U-Net

Accurate and efficient extraction of road information based on remote sensing image is a great significance for the establishment and maintenance of basic geographic databases. Due to the complex background information of high-resolution remote sensing images, existing algorithms cannot extract road information very well. U-Net network has good experimental results in image segmentation, but the accuracy of road segmentation results is not good. For this reason, this paper proposes a high-resolution image road extraction method based on improved U-Net network. Firstly, the U-Net-based network structure is designed and implemented. The network uses VGG16 as the network coding structure, which can extract feature semantic information better. Secondly, the use of Batch Normalization and Dropout solves the phenomenon of over-fitting that occurs during the network training process. Finally, the training data is expanded by rotation and mirror transformation, and the ELU activation function is used to improve the network training speed. The experimental results show that the method can extract road information more accurately and efficiently.