基于全卷积神经网络的多源高分辨率遥感道路提取

Road extraction from multi-source high resolution remote sensing image based on fully convolutional neural network

针对半自动道路提取方法人工参与较多、提取精度不高且较为耗时的问题提出一种基于全卷积神经网络(FCN)的多源高分辨率遥感道路提取方法。首先,对高分二号和World View图像进行分割,用卷积神经网络(CNN)分类出包含道路的图像;然后,用Canny算子提取道路的边缘特征信息;最后,结合RGB、Gray和标签图放入FCN中训练,将现有的FCN模型拓展为多卫星源输入及多特征源输入的FCN模型。选取西藏日喀则地区作为研究区域,实验结果显示,所提方法在对高分辨率遥感影像进行道路提取时能够达到99.2%的提取精度,并且有效地减少了提取所需的时间。

The semi-automatic road extraction method needs more artificial participation and is time-consuming, and its accuracy of road extraction is low. In order to solve the problems, a new method of road extraction from multi-source high resolution remote sensing image based on Fully Convolutional neural Network （FCN） was proposed. Firstly, the GF-2 and World View high resolution remote sensing images were divided into small pieces, the images containing roads were classified by Convolutional Neural Network （CNN）. Then, the Canny operator was used to extract the edge feature information of road. Finally, RGB, Gray and ground truth were combined and put into the FCN model for training, and the existing FCN model was extended to a new FCN model with multi-satellite source input and multi-feature source input. The Shigatse region of Tibet was chosen as the research area. The experimental results show that, the proposed method can achieve the extraction precision of 99.2% in the road extraction from high resolution remote sensing images, and effectively reduce the time needed for extraction.