Automatic road extraction method for high-resolution remote sensing images

作者：Liu Ruyi; Song Jianfeng; Quan Yining; Xu Pengfei; Xue Qing; Yang Yun; Miao Qiguang^*

来源：Journal of Xidian University, 2017, 44(1): 100-105.

DOI：10.3969/j.issn.1001-2400.2017.01.018

• Automatic road extraction
• Convolutional Neural Networks (CNN)
• Extraction algorithms
• High resolution remote sensing images
• High resolution satellite images
• Level Set
• Shape features
• Tensor voting

Road extraction from high-resolution satellite images is very important. Due to image noise, the natural scene complexity, and the extraction algorithms limitations, it still needs to be further researched. In recent years, level set evolution has been used to extract the road, but it is difficult to automatically generate initial level curves for the level set evolution (LSE). In this paper, we propose an automatic approach to the generation of initial level curves and use it to extract the road. Firstly, the convolutional neural network(CNN) is used to classify the road or nonroad, then shape features are adopted to filter nonlinear features to get the accurate road region. And on this basis, we exploit tensor voting to detect the road junctions and utilize them as initial level curves;finally we fuse the results obtained by the CNN and LSE. Experiments show that this algorithm can get an accurate and complete road.

• 出版日期2017
• 单位西北大学; 西安电子科技大学