A Road Extraction Method for Remote Sensing Image Based on Encoder-Decoder Network

Abstract

According to the characteristics of the road features, an Encoder-Decoder deep semantic segmentation network is designed for the road extraction of remote sensing images. Firstly, as the features of the road target are rich in local details and simple in semantic features, an Encoder-Decoder network with shallow layers and high resolution is designed to improve the ability to represent detail information. Secondly, as the road area is a small proportion in remote sensing images, the cross-entropy loss function is improved, which solves the imbalance between positive and negative samples in the training process. Experiments on large road extraction datasets show that the proposed method gets the recall rate 83.9%, precision 82.5% and F1-score 82.9%, which can extract the road targets in remote sensing images completely and accurately. The Encoder-Decoder network designed in this paper performs well in the road extraction task and needs less artificial participation, so it has a good application prospect.

Keywords

• |remote sensing|road extraction|deep learning|semantic segmentation|encoder-decoder network