A new road extraction method using Sentinel-1 SAR images based on the deep fully convolutional neural network

Abstract

ABSTRACT There is a pressing need for an automatic road extraction method due to the continuous development of transportation networks. Free from the influence of weather, satellite-mounted synthetic-aperture radar (SAR) opens the way for such a road detection application. This study introduces an automatic discrimination method based on a deep neural network (DNN) adjusted for roads from dual-polarimetric (VV and VH) Sentinel-1 SAR imagery. In this proposed method, the U-Net extended the convolutional neural network (CNN), is adjusted for road extraction from SAR images. To investigate the potential of using the U-Net based fully convolutional neural network (FCN) for road extraction, LeNet-5, is applied as a preliminary DNN model. Additionally, several training optimizations are introduced to improve accuracy. To assess the performance of the different polarization modes used in road extraction, both single-polarimetric and dual-polarimetric data were investigated. Moreover, four machine learning algorithms have been compared for accuracy and speed. As a result, the outcome evaluation of Precision, Recall, and F1 obtained by FCN is better than the original CNN, and the training time has been significantly reduced. The DNN model (CNN and FCN) is superior to machine learning methods in accuracy and elapsed computation time.