Road Extraction of High-Resolution Remote Sensing Images Derived from DenseUNet

Wu Fang,Xinchang Zhang,Zhiqiang Zhang,Jiang Xin

doi:10.3390/rs11212499

Abstract

Road network extraction is one of the significant assignments for disaster emergency response, intelligent transportation systems, and real-time updating road network. Road extraction base on high-resolution remote sensing images has become a hot topic. Presently, most of the researches are based on traditional machine learning algorithms, which are complex and computational because of impervious surfaces such as roads and buildings that are discernible in the images. Given the above problems, we propose a new method to extract the road network from remote sensing images using a DenseUNet model with few parameters and robust characteristics. DenseUNet consists of dense connection units and skips connections, which strengthens the fusion of different scales by connections at various network layers. The performance of the advanced method is validated on two datasets of high-resolution images by comparison with three classical semantic segmentation methods. The experimental results show that the method can be used for road extraction in complex scenes.

Highlights

The traffic road network is one of the essential geographic element of the urban system, which has critical applications in many fields, such as intelligent transportation, automobile navigation, and emergency support [1]
The deep convolutional network has been widely used in solving quite complex classification tasks, such as classification [22,23], semantic segmentation [24,25], and natural language processing [26,27]. These methods have proven to be profoundly robust to the appearance of different images, which prompted us to apply them to fully automated road segmentation in high-resolution remote sensing images
For the sake of quantitatively estimate the performance of the semantic segmentation method, we show the precision, recall, F1-Score, intersection over union (IoU) and Compared with the classical U-Net, SegNet, GL-Dense-U-Net, and FRRN-B network, we evaluated the proposed method on two urban scenario datasets: Conghua road dataset, and Massachusetts road dataset

Summary

Introduction

The traffic road network is one of the essential geographic element of the urban system, which has critical applications in many fields, such as intelligent transportation, automobile navigation, and emergency support [1]. The deep convolutional network has been widely used in solving quite complex classification tasks, such as classification [22,23], semantic segmentation [24,25], and natural language processing [26,27] Most importantly, these methods have proven to be profoundly robust to the appearance of different images, which prompted us to apply them to fully automated road segmentation in high-resolution remote sensing images. New segmentation methods based on deep neural networks and FCN were developed to extract roads from high-resolution remote sensing images. For irregular footprint problems between road area and image, Li et al [40] proposed a combining GANs and multi-scale context polymerization of semantic segmentation method, used for road extraction of UAV remote sensing images. The performance of the proposed method is validated by comparison with three classical semantic segmentation methods

Encoder–Decoder Architecture

DenseUNet

Software and Hardware Environment

Data Augmentation

Model Analysis

Conclusions