A Multiple Feature Fully Convolutional Network for Road Extraction From High-Resolution Remote Sensing Image Over Mountainous Areas

Abstract

Road extraction from the remote sensing image over mountainous areas is a difficult vision problem. In this letter, we propose a multiple feature fully convolutional network (MFFCN) on the basis of FCN for mountainous road extraction. The benefits of this model are twofold: first, MFFCN is a semantic segmentation model, which has deep convolutional networks. It avoids the problem of repeated storage and computational convolutions caused by the use of pixel blocks. Second, the MFFCN model could extract the spectral and terrain features. This method ensures the integrity and continuity of the road extraction results. The dataset is composed of GF-2 data and ASTER GDEM data in the Shigatse region of Tibet. We test our network on the dataset and compare it with four road extraction methods. The result shows that the proposed MFFCN is superior to all the comparing methods.