C-UNet: Complement UNet for Remote Sensing Road Extraction.

Yuewu Hou,Zhaoying Liu,Yujian Li,Ting Zhang

doi:10.3390/s21062153

Abstract

Roads are important mode of transportation, which are very convenient for people’s daily work and life. However, it is challenging to accuratly extract road information from a high-resolution remote sensing image. This paper presents a road extraction method for remote sensing images with a complement UNet (C-UNet). C-UNet contains four modules. Firstly, the standard UNet is used to roughly extract road information from remote sensing images, getting the first segmentation result; secondly, a fixed threshold is utilized to erase partial extracted information; thirdly, a multi-scale dense dilated convolution UNet (MD-UNet) is introduced to discover the complement road areas in the erased masks, obtaining the second segmentation result; and, finally, we fuse the extraction results of the first and the third modules, getting the final segmentation results. Experimental results on the Massachusetts Road dataset indicate that our C-UNet gets the higher results than the state-of-the-art methods, demonstrating its effectiveness.

Highlights

Remote Sensing Road Extraction.Road, as a vital special feature in remote sensing images, includes highways, urbanrural roads, byway, and so on
To improve the accuracy of remote sensing image road extraction, we propose a complement UNet model, called C-UNet, for high-resolution remote sensing image road extraction
Massachusetts Road dataset is usually used for road extraction of remote sensing images [42]

Summary

Introduction

Remote Sensing Road Extraction.Road, as a vital special feature in remote sensing images, includes highways, urbanrural roads, byway, and so on. Road extraction has important significance in many fields, such as automatic road navigation, disaster relief, urban planning, and geographic information update [1]. It is a challenging task because of the noise, occlusions, and complexity of the strcture of roads in remote sensing image [2]. The contrast of infrared images is low, lacking of image details This disadvantages make it difficult to extract road segmentation [3]. Remote sensing images are less limited by ground conditions, real time transmission, and its detection range is large All these advantages make it more suitable to extract roads

Methods

Results

Discussion

Conclusion